* using log directory 'd:/Rcompile/CRANpkg/local/4.5/rtemis.Rcheck'
* using R version 4.5.3 (2026-03-11 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.3.0
    GNU Fortran (GCC) 14.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'rtemis/DESCRIPTION' ... OK
* this is package 'rtemis' version '1.2.7'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'rtemis' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [1s] OK
* checking whether the package can be loaded with stated dependencies ... [1s] OK
* checking whether the package can be unloaded cleanly ... [1s] OK
* checking whether the namespace can be loaded with stated dependencies ... [1s] OK
* checking whether the namespace can be unloaded cleanly ... [2s] OK
* checking loading without being on the library search path ... [2s] OK
* checking whether startup messages can be suppressed ... [2s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [36s] OK
* checking Rd files ... [4s] OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... [0s] OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking examples ... [82s] OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [167s] ERROR
  Running 'testthat.R' [166s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(rtemis)
    .:rtemis 1.2.7 🌊 x86_64-w64-mingw32/x64
  > library(testthat)
  
  Attaching package: 'testthat'
  
  The following object is masked from 'package:rtemis':
  
      describe
  
  > 
  > test_check("rtemis")
  2026-10-08 02:49:20 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:20 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:20 Using max n bins possible = 2. [kfold]
  2026-10-08 02:49:20 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:20 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:21 ✓ Created file: D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\rtemis_cluster.json [write_lines]
  2026-10-08 02:49:21 ✖ rtemis_range_error [setup_KMeans]
  2026-10-08 02:49:21 ▶ [cluster]
  2026-10-08 02:49:21 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:21 Clustering with KMeans... [cluster]
  2026-10-08 02:49:21 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:21 Clustering with KMeans ... [cluster_]
  2026-10-08 02:49:21 ✓ Done in 0.36 seconds. [cluster]
  2026-10-08 02:49:21 ▶ [cluster]
  2026-10-08 02:49:21 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:21 Clustering with KMeans... [cluster]
  2026-10-08 02:49:21 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:21 Clustering with KMeans ... [cluster_]
  2026-10-08 02:49:21 ✓ Done in 0.16 seconds. [cluster]
  2026-10-08 02:49:21 ▶ [cluster]
  2026-10-08 02:49:21 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:21 Clustering with HardCL... [cluster]
  2026-10-08 02:49:21 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:21 Clustering with HardCL ... [cluster_]
  2026-10-08 02:49:21 ✓ Done in 0.03 seconds. [cluster]
  2026-10-08 02:49:22 ▶ [cluster]
  2026-10-08 02:49:22 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:22 Clustering with NeuralGas... [cluster]
  2026-10-08 02:49:22 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:22 Clustering with NeuralGas ... [cluster_]
  2026-10-08 02:49:22 ✓ Done in 0.05 seconds. [cluster]
  2026-10-08 02:49:22 ▶ [cluster]
  2026-10-08 02:49:22 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:22 Clustering with CMeans... [cluster]
  2026-10-08 02:49:22 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:22 Clustering with CMeans ... [cluster_]
  Iteration:   1, Error:  1.0222276897
  Iteration:   2, Error:  0.4426354825
  Iteration:   3, Error:  0.4040115623
  Iteration:   4, Error:  0.4035611242
  Iteration:   5, Error:  0.4034484182
  Iteration:   6, Error:  0.4034031102
  Iteration:   7, Error:  0.4033845090
  Iteration:   8, Error:  0.4033768332
  Iteration:   9, Error:  0.4033736566
  Iteration:  10, Error:  0.4033723396
  Iteration:  11, Error:  0.4033717929
  Iteration:  12, Error:  0.4033715658
  Iteration:  13, Error:  0.4033714714
  Iteration:  14, Error:  0.4033714321
  Iteration:  15, Error:  0.4033714158
  Iteration:  16, Error:  0.4033714090
  Iteration:  17 converged, Error:  0.4033714062
  2026-10-08 02:49:22 ✓ Done in 0.01 seconds. [cluster]
  2026-10-08 02:49:22 ▶ [cluster]
  2026-10-08 02:49:22 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:22 Clustering with DBSCAN... [cluster]
  2026-10-08 02:49:22 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:22 Clustering with DBSCAN ... [cluster_]
  Saving _problems/test_Clustering-100.R
  2026-10-08 02:49:22 ✓ Created file: D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\rtemis_decompose.json [write_lines]
  2026-10-08 02:49:22 ▶ [decomp]
  2026-10-08 02:49:22 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:22 Decomposing with PCA... [decomp]
  2026-10-08 02:49:22 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:22 Decomposing with PCA ... [decomp_]
  2026-10-08 02:49:22 ✓ Done in 0.02 seconds. [decomp]
  2026-10-08 02:49:23 ▶ [decomp]
  2026-10-08 02:49:23 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:23 Decomposing with ICA... [decomp]
  2026-10-08 02:49:23 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:23 Decomposing with ICA ... [decomp_]
  Centering
  colstandard
  Whitening
  Symmetric FastICA using logcosh approx. to neg-entropy function
  Iteration 1 tol=0.038537
  Iteration 2 tol=0.004207
  Iteration 3 tol=0.002599
  Iteration 4 tol=0.001640
  Iteration 5 tol=0.000951
  Iteration 6 tol=0.000483
  Iteration 7 tol=0.000217
  Iteration 8 tol=0.000088
  2026-10-08 02:49:23 ✓ Done in 0.01 seconds. [decomp]
  2026-10-08 02:49:25 ▶ [decomp]
  2026-10-08 02:49:25 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:25 Decomposing with NMF... [decomp]
  2026-10-08 02:49:25 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:25 Decomposing with NMF ... [decomp_]
  2026-10-08 02:49:28 ✓ Done in 3.31 seconds. [decomp]
  2026-10-08 02:49:28 ▶ [decomp]
  2026-10-08 02:49:28 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:28 Decomposing with UMAP... [decomp]
  2026-10-08 02:49:28 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:28 Decomposing with UMAP ... [decomp_]
  2026-10-08 02:49:31 ✓ Done in 2.90 seconds. [decomp]
  2026-10-08 02:49:31 ▶ [decomp]
  2026-10-08 02:49:31 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:31 Decomposing with UMAP... [decomp]
  2026-10-08 02:49:31 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:31 Decomposing with UMAP ... [decomp_]
  2026-10-08 02:49:33 ✓ Done in 1.74 seconds. [decomp]
  2026-10-08 02:49:33 ▶ [decomp]
  2026-10-08 02:49:33 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:33 Decomposing with tSNE... [decomp]
  2026-10-08 02:49:33 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:33 Decomposing with tSNE ... [decomp_]
  2026-10-08 02:49:33 Removing 1 duplicate case... [preprocess]
  2026-10-08 02:49:33 Preprocessing done. [preprocess]
  2026-10-08 02:49:33 ▶ [decomp]
  2026-10-08 02:49:33 Input: 149 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:33 Decomposing with tSNE... [decomp]
  2026-10-08 02:49:33 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:33 Decomposing with tSNE ... [decomp_]
  2026-10-08 02:49:34 ✓ Done in 0.48 seconds. [decomp]
  2026-10-08 02:49:34 ▶ [decomp]
  2026-10-08 02:49:34 Input: 150 cases x 4 features. [summarize_unsupervised]
  2026-10-08 02:49:34 Decomposing with Isomap... [decomp]
  2026-10-08 02:49:34 Checking unsupervised data... ✔ [check_unsupervised_data]
  2026-10-08 02:49:34 Decomposing with Isomap ... [decomp_]
  2026-10-08 02:49:34 ✓ Done in 0.04 seconds. [decomp]
  2026-10-08 02:49:35 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:35 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:36 Scaling and centering 4 numeric features... [preprocess]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 Scaling and centering 4 numeric features... [preprocess]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 Scaling and centering 4 numeric features... [preprocess]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 Applying preprocessing to test data... [preprocess]
  2026-10-08 02:49:36 Scaling and centering 4 numeric features... [preprocess]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 Imputing missing values using get_mode (discrete) and mean (continuous)... [preprocess]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 One hot encoding g... ✔ [one_hot]
  2026-10-08 02:49:36 Preprocessing done. [preprocess]
  2026-10-08 02:49:36 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:36 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:36 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:36 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:36 ✓ Created file: D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\rtemis_super.json [write_lines]
  2026-10-08 02:49:36 ✓ Created file: D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\rtemis_decom.json [write_lines]
  2026-10-08 02:49:37 ✓ Created file: D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\rtemis_clust.json [write_lines]
  2026-10-08 02:49:37 ✖ rtemis_value_error [.detect_config_kind]
  2026-10-08 02:49:37 ✖ rtemis_value_error [.detect_config_kind]
  2026-10-08 02:49:37 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:37 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:37 Using max n bins possible = 2. [kfold]
  2026-10-08 02:49:37 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:37 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:37 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:37 ▶ [train]
  2026-10-08 02:49:37 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:37     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:37 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:37 Training GLM Regression... [train]
  2026-10-08 02:49:37 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLM (Generalized Linear Model)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.82
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.74
     MSE: 1.03
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:49:37 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:49:37 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:37 ▶ [train]
  2026-10-08 02:49:37 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:37     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:37 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:37 Training GLM Regression... [train]
  2026-10-08 02:49:37 Checking data is ready for training...
  2026-10-08 02:49:37 ✖ rtemis_missing_data [check_supervised]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:38 ▶ [train]
  2026-10-08 02:49:38 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:38 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:49:38 <> Training GLM Regression using 3 independent folds... [train]
  2026-10-08 02:49:38 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:38 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  GLM (Generalized Linear Model)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.723 (0.010)
     MSE: 0.833 (0.031)
    RMSE: 0.913 (0.017)
      R²: 0.830 (0.009)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.747 (0.023)
     MSE: 0.892 (0.065)
    RMSE: 0.944 (0.034)
      R²: 0.817 (0.017)
  
  2026-10-08 02:49:38 ✓ Done in 0.41 seconds. [train]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:38 ▶ [train]
  2026-10-08 02:49:38 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:38     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:38 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:38 Training GLM Classification... [train]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLM (Generalized Linear Model)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           1
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.978  
          Specificity  0.978  
    Balanced Accuracy  0.978  
                  Ppv  0.978  
                  Npv  0.978  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.998  
          Brier Score  0.018  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.093  
  
       Positive Class virginica
  
  2026-10-08 02:49:38 ✓ Done in 0.16 seconds. [train]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:38 ▶ [train]
  2026-10-08 02:49:38 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:38     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:38 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:38 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:49:38 Training GLM Classification... [train]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLM (Generalized Linear Model)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           1
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.978  
          Specificity  0.978  
    Balanced Accuracy  0.978  
                  Ppv  0.978  
                  Npv  0.978  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.998  
          Brier Score  0.018  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.093  
  
       Positive Class virginica
  
  2026-10-08 02:49:38 ✓ Done in 0.11 seconds. [train]
  2026-10-08 02:49:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:38 ▶ [train]
  2026-10-08 02:49:38 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:38 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:49:38 <> Training GLM Classification using 3 independent folds... [train]
  2026-10-08 02:49:38 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:38 Using max n bins possible = 2. [kfold]
  2026-10-08 02:49:39 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  GLM (Generalized Linear Model)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         99           1
           versicolor          1          99
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.990 (0.017)
          Specificity: 0.990 (0.017)
    Balanced Accuracy: 0.990 (0.017)
                  Ppv: 0.990 (0.017)
                  Npv: 0.990 (0.017)
                   F1: 0.990 (0.017)
             Accuracy: 0.990 (0.017)
                  Auc: 0.998 (3.7e-03)
          Brier Score: 0.007 (0.012)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           6
           versicolor          5          45
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.877 (0.125)
          Specificity: 0.898 (0.095)
    Balanced Accuracy: 0.888 (0.092)
                  Ppv: 0.898 (0.100)
                  Npv: 0.886 (0.118)
                   F1: 0.885 (0.096)
             Accuracy: 0.888 (0.092)
                  Auc: 0.939 (0.062)
          Brier Score: 0.101 (0.088)
  
  2026-10-08 02:49:39 ✓ Done in 0.57 seconds. [train]
  2026-10-08 02:49:39 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:39 ▶ [train]
  2026-10-08 02:49:39 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:39     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:39 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:39 Training GLMNET Regression... [train]
  2026-10-08 02:49:39 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.82
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.74
     MSE: 1.02
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:49:39 ✓ Done in 0.16 seconds. [train]
  2026-10-08 02:49:39 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:39 ▶ [train]
  2026-10-08 02:49:39 ▶ train GLMNET Regression [session_render]
  2026-10-08 02:49:39 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:39     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:39 // Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:39   ▶ tune [session_render]
  
  2026-10-08 02:49:39 ▶ [tune_GridSearch]
  2026-10-08 02:49:39 <> Tuning GLMNET by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:49:39 1 parameter combination x 5 resamples: 5 models total (). [tune_GridSearch]
  2026-10-08 02:49:39 Input contains more than one column; stratifying on last. [resample]
  <KFoldConfig>
             n: 5
  stratify_var: NULL
  strat_n_bins: 4
      id_strat: NULL
          seed: NULL
  2026-10-08 02:49:40 Tuning using future (mirai_multisession); N workers: 2 [tune_GridSearch]
  2026-10-08 02:49:40 ℹ Current future plan: [tune_GridSearch]
  mirai_multisession:
  - args: function (..., workers = 2L, envir = parent.frame())
  - tweaked: TRUE
  - call: future::plan(strategy = requested_plan, workers = n_workers)
  MiraiMultisessionFutureBackend:
  Inherits: MiraiFutureBackend, MultiprocessFutureBackend, FutureBackend
  UUID: a6c1abac52bfc9ebd756a9cc8aa0b17f
  Number of workers: 2
  Number of free workers: 2
  Available cores: 2
  Automatic garbage collection: FALSE
  Early signaling: FALSE
  Interrupts are enabled: TRUE
  Maximum total size of globals: +Inf
  Maximum total size of value: +Inf
  Number of active futures: 0
  Number of futures since start: 0 (0 created, 0 launched, 0 finished)
  Total runtime of futures: 0 secs (NaN secs/finished future)
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.77
     MSE: 0.94
    RMSE: 0.97
      R²: 0.81
    <Validation Regression Metrics>
     MAE: 0.78
     MSE: 0.91
    RMSE: 0.96
      R²: 0.83
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.75
     MSE: 0.90
    RMSE: 0.95
      R²: 0.82
    <Validation Regression Metrics>
     MAE: 0.75
     MSE: 0.87
    RMSE: 0.93
      R²: 0.80
  2026-10-08 02:49:41 ℹ Running grid line #1/5... [tune_GridSearch]
  2026-10-08 02:49:41 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:42 ▶ [train]
  2026-10-08 02:49:42   Training set: 285 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:42 Validation set: 73 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:42 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:42 Training GLMNET Regression... [train]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  
  
  2026-10-08 02:49:44 ✓ Done in 2.72 seconds. [train]
  2026-10-08 02:49:44 ℹ Running grid line #2/5... [tune_GridSearch]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:44 ▶ [train]
  2026-10-08 02:49:44   Training set: 288 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 Validation set: 70 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:44 Training GLMNET Regression... [train]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  
  
  2026-10-08 02:49:44 ✓ Done in 0.21 seconds. [train]
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.78
     MSE: 0.93
    RMSE: 0.97
      R²: 0.81
    <Validation Regression Metrics>
     MAE: 0.68
     MSE: 0.80
    RMSE: 0.89
      R²: 0.83
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.88
    RMSE: 0.94
      R²: 0.82
    <Validation Regression Metrics>
     MAE: 0.79
     MSE: 0.85
    RMSE: 0.92
      R²: 0.83
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.82
    RMSE: 0.91
      R²: 0.83
    <Validation Regression Metrics>
     MAE: 0.82
     MSE: 1.14
    RMSE: 1.07
      R²: 0.79
  2026-10-08 02:49:41 ℹ Running grid line #3/5... [tune_GridSearch]
  2026-10-08 02:49:41 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:41 ▶ [train]
  2026-10-08 02:49:41   Training set: 286 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:41 Validation set: 72 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:41 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:41 Training GLMNET Regression... [train]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  
  
  2026-10-08 02:49:44 ✓ Done in 2.45 seconds. [train]
  2026-10-08 02:49:44 ℹ Running grid line #4/5... [tune_GridSearch]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:44 ▶ [train]
  2026-10-08 02:49:44   Training set: 288 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 Validation set: 70 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:44 Training GLMNET Regression... [train]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  
  
  2026-10-08 02:49:44 ✓ Done in 0.23 seconds. [train]
  2026-10-08 02:49:44 ℹ Running grid line #5/5... [tune_GridSearch]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:44 ▶ [train]
  2026-10-08 02:49:44   Training set: 285 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 Validation set: 73 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:44 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:44 Training GLMNET Regression... [train]
  2026-10-08 02:49:44 Checking data is ready for training... ✔ [check_supervised]
  
  
  2026-10-08 02:49:44 ✓ Done in 0.17 seconds. [train]
  2026-10-08 02:49:45 ℹ Extracting best lambda from GLMNET models... [tune_GridSearch]
  2026-10-08 02:49:45 Best config to minimize mse: [tune_GridSearch]
                        lambda: {} => 0.140014948415186
  2026-10-08 02:49:45 ✓ Done in 5.22 seconds. [tune_GridSearch]
  2026-10-08 02:49:45 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:49:45   ✔ tune (5.5 s) [session_render]
  2026-10-08 02:49:45 Training GLMNET Regression with tuned hyperparameters... [train]
  2026-10-08 02:49:45   ▶ train_alg GLMNET [session_render]
  2026-10-08 02:49:45 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:45 ℹ NCOL(xm): 6 [train_]
  2026-10-08 02:49:45 ℹ Updated hyperparameters[["penalty_factor"]] to all 1s. [train_]
  2026-10-08 02:49:45   ✔ train_alg GLMNET (82 ms) [session_render]
  2026-10-08 02:49:45   ▶ predict [session_render]
  2026-10-08 02:49:45   ✔ predict (8 ms) [session_render]
  2026-10-08 02:49:45   ▶ varimp GLMNET [session_render]
  2026-10-08 02:49:45   ✔ varimp GLMNET (5 ms) [session_render]
  2026-10-08 02:49:45   ▶ metrics [session_render]
  2026-10-08 02:49:45   ✔ metrics (70 ms) [session_render]
  2026-10-08 02:49:45 ✔ train GLMNET Regression (5.7 s) [session_render]
  
  <Regression>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.75
     MSE: 0.90
    RMSE: 0.95
      R²: 0.82
  
    <Test Regression Metrics>
     MAE: 0.73
     MSE: 0.95
    RMSE: 0.98
      R²: 0.79
  
  2026-10-08 02:49:45 Models trained: [session_report]
  2026-10-08 02:49:45   tuning:  1 combos ⨉ 5 inner resamples = 5 [session_report]
  2026-10-08 02:49:45   + final: 1 per fit = 1 [session_report]
  2026-10-08 02:49:45   total = 6 models [session_report]
  2026-10-08 02:49:45   6 succeeded in 5.7 s [session_report]
  2026-10-08 02:49:45 ✓ Done in 5.70 seconds. [train]
  2026-10-08 02:49:45 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:45 ▶ [train]
  2026-10-08 02:49:45 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:45     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:45 // Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:45 ✖ rtemis_value_error [tune_GridSearch]
  2026-10-08 02:49:45 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:45 ▶ [train]
  2026-10-08 02:49:45 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:45     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:45 // Max workers: 2 { Algorithm: 1; Tuning: 2; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:45 <> Tuning GLMNET by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:49:45 1 parameter combination x 5 resamples: 5 models total (). [tune_GridSearch]
  2026-10-08 02:49:45 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:45 Tuning using mirai; N workers: 2 [tune_GridSearch]
  ======>------------------------   20% | ETA: 14s
  ==============================>  100% | ETA:  0s
  2026-10-08 02:49:49 Best config to minimize mse: [tune_GridSearch]
                        lambda: {} => 0.15238782160033
  2026-10-08 02:49:49 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:49:49 Training GLMNET Regression with tuned hyperparameters... [train]
  2026-10-08 02:49:49 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.76
     MSE: 0.91
    RMSE: 0.95
      R²: 0.82
  
    <Test Regression Metrics>
     MAE: 0.74
     MSE: 0.95
    RMSE: 0.97
      R²: 0.79
  
  2026-10-08 02:49:50 ✓ Done in 4.50 seconds. [train]
  2026-10-08 02:49:50 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:50 ▶ [train]
  2026-10-08 02:49:50 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:50     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:50 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:50 <> Tuning GLMNET by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:49:50 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:49:50 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:50 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:49:52 Best config to minimize mse: [tune_GridSearch]
                         alpha: {0, 1} => 1
                        lambda: {} => 0.134770240620737
  2026-10-08 02:49:52 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:49:52 Training GLMNET Regression with tuned hyperparameters... [train]
  2026-10-08 02:49:52 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.75
     MSE: 0.89
    RMSE: 0.95
      R²: 0.82
  
    <Test Regression Metrics>
     MAE: 0.73
     MSE: 0.96
    RMSE: 0.98
      R²: 0.79
  
  2026-10-08 02:49:52 ✓ Done in 2.35 seconds. [train]
  2026-10-08 02:49:52 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:52 ▶ [train]
  2026-10-08 02:49:52 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:49:52 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:49:52 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:52 <> Training GLMNET Regression using 3 independent folds... [train]
  2026-10-08 02:49:52 Input contains more than one column; stratifying on last. [resample]
  \ 1/3 ETA:  5s | Training outer resamples... 
  2026-10-08 02:49:58 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.760 (0.030)
     MSE: 0.910 (0.042)
    RMSE: 0.954 (0.022)
      R²: 0.816 (0.011)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.766 (0.056)
     MSE: 0.921 (0.077)
    RMSE: 0.959 (0.040)
      R²: 0.813 (0.021)
  
  2026-10-08 02:49:59 ✓ Done in 6.60 seconds. [train]
  2026-10-08 02:49:59 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:59 ▶ [train]
  2026-10-08 02:49:59 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:59     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:49:59 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:59 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:49:59 Training GLMNET Classification... [train]
  2026-10-08 02:49:59 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLMNET (Elastic Net)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         43           2
           versicolor          2          43
  
                       Overall  
          Sensitivity  0.956  
          Specificity  0.956  
    Balanced Accuracy  0.956  
                  Ppv  0.956  
                  Npv  0.956  
                   F1  0.956  
             Accuracy  0.956  
                  Auc  0.998  
          Brier Score  0.024  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.055  
  
       Positive Class virginica
  
  2026-10-08 02:49:59 ✓ Done in 0.13 seconds. [train]
  2026-10-08 02:49:59 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:49:59 ▶ [train]
  2026-10-08 02:49:59 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:49:59     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:49:59 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:49:59 <> Tuning GLMNET by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:49:59 1 parameter combination x 5 resamples: 5 models total (). [tune_GridSearch]
  2026-10-08 02:49:59 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:49:59 Using max n bins possible = 3. [kfold]
  2026-10-08 02:49:59 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:03 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        lambda: {} => 0.00821962912592355
  2026-10-08 02:50:03 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:03 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:50:03 Training GLMNET Classification with tuned hyperparameters... [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          42          3
            virginica       0           2         43
  
                       Overall  
    Balanced Accuracy  0.963  
                   F1  0.963  
             Accuracy  0.963  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.933       0.956    
          Specificity  1.000   0.978       0.967    
    Balanced Accuracy  1.000   0.956       0.961    
                  Ppv  1.000   0.955       0.935    
                  Npv  1.000   0.967       0.978    
                   F1  1.000   0.944       0.945    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           1          4
  
                       Overall  
    Balanced Accuracy  0.933  
                   F1  0.933  
             Accuracy  0.933  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       0.800    
          Specificity  1.000   0.900       1.000    
    Balanced Accuracy  1.000   0.950       0.900    
                  Ppv  1.000   0.833       1.000    
                  Npv  1.000   1.000       0.909    
                   F1  1.000   0.909       0.889    
  
  2026-10-08 02:50:03 ✓ Done in 3.92 seconds. [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:03 ▶ [train]
  2026-10-08 02:50:03 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:03     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:03 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:03 Training GAM Regression... [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GAM (Generalized Additive Model)
  
    <Training Regression Metrics>
     MAE: 0.72
     MSE: 0.80
    RMSE: 0.89
      R²: 0.84
  
    <Test Regression Metrics>
     MAE: 0.75
     MSE: 1.01
    RMSE: 1.00
      R²: 0.77
  
  2026-10-08 02:50:03 ✓ Done in 0.25 seconds. [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:03 ▶ [train]
  2026-10-08 02:50:03 Training set: 358 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:03     Test set: 42 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:03 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:03 Training GAM Regression... [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GAM (Generalized Additive Model)
  
    <Training Regression Metrics>
     MAE: 1.49
     MSE: 2.95
    RMSE: 1.72
      R²: 0.40
  
    <Test Regression Metrics>
     MAE: 1.26
     MSE: 2.20
    RMSE: 1.48
      R²: 0.51
  
  2026-10-08 02:50:03 ✓ Done in 0.30 seconds. [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:03 ▶ [train]
  2026-10-08 02:50:03 Training set: 358 cases x 1 features. [summarize_supervised]
  2026-10-08 02:50:03     Test set: 42 cases x 1 features. [summarize_supervised]
  2026-10-08 02:50:03 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:03 Training GAM Regression... [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GAM (Generalized Additive Model)
  
    <Training Regression Metrics>
     MAE: 1.37
     MSE: 2.80
    RMSE: 1.67
      R²: 0.43
  
    <Test Regression Metrics>
     MAE: 1.16
     MSE: 1.93
    RMSE: 1.39
      R²: 0.57
  
  2026-10-08 02:50:03 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:50:03 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:03 ▶ [train]
  2026-10-08 02:50:03 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:03     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:03 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:03 <> Tuning GAM by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:03 3 parameter combinations x 5 resamples: 15 models total (). [tune_GridSearch]
  2026-10-08 02:50:03 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:03 Tuning in sequence [tune_GridSearch]
  \ 14/15 ETA:  0s | Tuning... (15 combinations) 
  2026-10-08 02:50:06 Best config to minimize mse: [tune_GridSearch]
                        k: {3, 5, 7} => 3
  2026-10-08 02:50:06 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:06 Training GAM Regression with tuned hyperparameters... [train]
  2026-10-08 02:50:06 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GAM (Generalized Additive Model)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.72
     MSE: 0.82
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.74
     MSE: 1.02
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:50:07 ✓ Done in 3.03 seconds. [train]
  2026-10-08 02:50:07 ✖ rtemis_dim_error [predict_supervised_]
  2026-10-08 02:50:07 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:07 ▶ [train]
  2026-10-08 02:50:07 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:07 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:50:07 <> Training GAM Regression using 3 independent folds... [train]
  2026-10-08 02:50:07 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:07 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  GAM (Generalized Additive Model)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.709 (0.025)
     MSE: 0.796 (0.067)
    RMSE: 0.891 (0.038)
      R²: 0.838 (0.012)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.759 (0.059)
     MSE: 0.908 (0.136)
    RMSE: 0.951 (0.070)
      R²: 0.815 (0.024)
  
  2026-10-08 02:50:07 ✓ Done in 0.77 seconds. [train]
  2026-10-08 02:50:07 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:07 ▶ [train]
  2026-10-08 02:50:07 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:07     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:07 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:07 Training GAM Classification... [train]
  2026-10-08 02:50:07 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GAM (Generalized Additive Model)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          0          45
  
                       Overall  
          Sensitivity  1.000  
          Specificity  1.000  
    Balanced Accuracy  1.000  
                  Ppv  1.000  
                  Npv  1.000  
                   F1  1.000  
             Accuracy  1.000  
                  Auc  1.000  
          Brier Score  2.2e-06
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.100  
  
       Positive Class virginica
  
  2026-10-08 02:50:08 ✓ Done in 0.33 seconds. [train]
  2026-10-08 02:50:08 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:08 ▶ [train]
  2026-10-08 02:50:08 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:08     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:08 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:08 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:50:08 Training GAM Classification... [train]
  2026-10-08 02:50:08 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GAM (Generalized Additive Model)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          0          45
  
                       Overall  
          Sensitivity  1.000  
          Specificity  1.000  
    Balanced Accuracy  1.000  
                  Ppv  1.000  
                  Npv  1.000  
                   F1  1.000  
             Accuracy  1.000  
                  Auc  1.000  
          Brier Score  2.2e-06
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.100  
  
       Positive Class virginica
  
  2026-10-08 02:50:08 ✓ Done in 0.37 seconds. [train]
  2026-10-08 02:50:08 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:08 ▶ [train]
  2026-10-08 02:50:08 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:08     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:08 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:08 Training LinearSVM Regression... [train]
  2026-10-08 02:50:08 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:08 One hot encoding g... ✔ [one_hot]
  2026-10-08 02:50:08 Preprocessing done. [preprocess]
  
  <Regression>
  LinearSVM (Support Vector Machine with Linear Kernel)
  
    <Training Regression Metrics>
     MAE: 0.72
     MSE: 0.83
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.75
     MSE: 1.02
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:50:08 ✓ Done in 0.24 seconds. [train]
  2026-10-08 02:50:08 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:08 ▶ [train]
  2026-10-08 02:50:08 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:08     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:08 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:08 <> Tuning LinearSVM by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:08 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:08 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:08 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:12 Best config to minimize mse: [tune_GridSearch]
                        cost: {1, 10} => 1
  2026-10-08 02:50:12 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:12 Training LinearSVM Regression with tuned hyperparameters... [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:12 One hot encoding g... ✔ [one_hot]
  2026-10-08 02:50:12 Preprocessing done. [preprocess]
  
  <Regression>
  LinearSVM (Support Vector Machine with Linear Kernel)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.72
     MSE: 0.83
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.75
     MSE: 1.02
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:50:12 ✓ Done in 3.36 seconds. [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:12 ▶ [train]
  2026-10-08 02:50:12 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:12 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:50:12 <> Training LinearSVM Regression using 3 independent folds... [train]
  2026-10-08 02:50:12 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:12 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  LinearSVM (Support Vector Machine with Linear Kernel)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.723 (0.021)
     MSE: 0.845 (0.022)
    RMSE: 0.919 (0.012)
      R²: 0.827 (0.008)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.741 (0.043)
     MSE: 0.875 (0.056)
    RMSE: 0.935 (0.030)
      R²: 0.821 (0.019)
  
  2026-10-08 02:50:12 ✓ Done in 0.54 seconds. [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:12 ▶ [train]
  2026-10-08 02:50:12 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:12     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:12 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:12 Training LinearSVM Classification... [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:12 One hot encoding gn... ✔ [one_hot]
  2026-10-08 02:50:12 Preprocessing done. [preprocess]
  
  <Classification>
  LinearSVM (Support Vector Machine with Linear Kernel)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.999  
          Brier Score  0.023  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.041  
  
       Positive Class virginica
  
  2026-10-08 02:50:12 ✓ Done in 0.12 seconds. [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:12 ▶ [train]
  2026-10-08 02:50:12 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:12     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:12 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:12 Training LinearSVM Classification... [train]
  2026-10-08 02:50:12 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  LinearSVM (Support Vector Machine with Linear Kernel)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          42          3
            virginica       0           1         44
  
                       Overall  
    Balanced Accuracy  0.970  
                   F1  0.970  
             Accuracy  0.970  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.933       0.978    
          Specificity  1.000   0.989       0.967    
    Balanced Accuracy  1.000   0.961       0.972    
                  Ppv  1.000   0.977       0.936    
                  Npv  1.000   0.967       0.989    
                   F1  1.000   0.955       0.957    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           0          5
  
                       Overall  
    Balanced Accuracy  1.000  
                   F1  1.000  
             Accuracy  1.000  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       1.000    
          Specificity  1.000   1.000       1.000    
    Balanced Accuracy  1.000   1.000       1.000    
                  Ppv  1.000   1.000       1.000    
                  Npv  1.000   1.000       1.000    
                   F1  1.000   1.000       1.000    
  
  2026-10-08 02:50:13 ✓ Done in 0.12 seconds. [train]
  2026-10-08 02:50:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:13 ▶ [train]
  2026-10-08 02:50:13 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:13 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:13 <> Training LinearSVM Classification using 3 independent folds... [train]
  2026-10-08 02:50:13 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:13 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:13 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  LinearSVM (Support Vector Machine with Linear Kernel)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica        100           0
           versicolor          6          94
  
    Showing mean (sd) across resamples.
          Sensitivity: 1.000 (0.000)
          Specificity: 0.940 (0.030)
    Balanced Accuracy: 0.970 (0.015)
                  Ppv: 0.944 (0.027)
                  Npv: 1.000 (0.000)
                   F1: 0.971 (0.014)
             Accuracy: 0.970 (0.015)
                  Auc: 0.998 (4.9e-04)
          Brier Score: 0.029 (0.006)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         49           1
           versicolor          3          47
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.979 (0.036)
          Specificity: 0.941 (0.059)
    Balanced Accuracy: 0.960 (0.016)
                  Ppv: 0.946 (0.053)
                  Npv: 0.980 (0.034)
                   F1: 0.961 (0.015)
             Accuracy: 0.960 (0.016)
                  Auc: 0.998 (4e-03)
          Brier Score: 0.034 (1.7e-03)
  
  2026-10-08 02:50:13 ✓ Done in 0.32 seconds. [train]
  2026-10-08 02:50:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:13 ▶ [train]
  2026-10-08 02:50:13 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:13     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:13 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:13 Training RadialSVM Regression... [train]
  2026-10-08 02:50:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:13 One hot encoding g... ✔ [one_hot]
  2026-10-08 02:50:13 Preprocessing done. [preprocess]
  
  <Regression>
  RadialSVM (Support Vector Machine with Radial Kernel)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.84
    RMSE: 0.92
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.75
     MSE: 1.01
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:50:13 ✓ Done in 0.20 seconds. [train]
  2026-10-08 02:50:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:13 ▶ [train]
  2026-10-08 02:50:13 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:13     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:13 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:13 <> Tuning RadialSVM by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:13 3 parameter combinations x 5 resamples: 15 models total (). [tune_GridSearch]
  2026-10-08 02:50:13 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:13 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:16 Best config to minimize mse: [tune_GridSearch]
                        cost: {1, 10, 100} => 1
  2026-10-08 02:50:16 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:16 Training RadialSVM Regression with tuned hyperparameters... [train]
  2026-10-08 02:50:16 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:16 One hot encoding g... ✔ [one_hot]
  2026-10-08 02:50:16 Preprocessing done. [preprocess]
  
  <Regression>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.84
    RMSE: 0.92
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.75
     MSE: 1.01
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:50:16 ✓ Done in 3.23 seconds. [train]
  2026-10-08 02:50:16 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:16 ▶ [train]
  2026-10-08 02:50:16 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:16 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:16 <> Training RadialSVM Regression using 3 independent folds... [train]
  2026-10-08 02:50:16 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:17 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.729 (0.014)
     MSE: 0.861 (0.011)
    RMSE: 0.928 (0.006)
      R²: 0.824 (2.9e-03)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.768 (0.032)
     MSE: 0.938 (0.030)
    RMSE: 0.968 (0.016)
      R²: 0.809 (0.006)
  
  2026-10-08 02:50:17 ✓ Done in 0.58 seconds. [train]
  2026-10-08 02:50:17 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:17 ▶ [train]
  2026-10-08 02:50:17 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:17 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:50:17 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:17 <> Training RadialSVM Regression using 3 independent folds... [train]
  2026-10-08 02:50:17 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:22 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.713 (0.024)
     MSE: 0.837 (0.051)
    RMSE: 0.915 (0.028)
      R²: 0.829 (0.011)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.787 (0.005)
     MSE: 0.963 (3.8e-03)
    RMSE: 0.981 (1.9e-03)
      R²: 0.803 (0.007)
  
  2026-10-08 02:50:22 ✓ Done in 4.78 seconds. [train]
  2026-10-08 02:50:22 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:22 ▶ [train]
  2026-10-08 02:50:22 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:22     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:22 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:22 Training RadialSVM Classification... [train]
  2026-10-08 02:50:22 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:22 One hot encoding gn... ✔ [one_hot]
  2026-10-08 02:50:22 Preprocessing done. [preprocess]
  
  <Classification>
  RadialSVM (Support Vector Machine with Radial Kernel)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         42           3
           versicolor          2          43
  
                       Overall  
          Sensitivity  0.933  
          Specificity  0.956  
    Balanced Accuracy  0.944  
                  Ppv  0.955  
                  Npv  0.935  
                   F1  0.944  
             Accuracy  0.944  
                  Auc  0.995  
          Brier Score  0.035  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.059  
  
       Positive Class virginica
  
  2026-10-08 02:50:22 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:50:22 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:22 ▶ [train]
  2026-10-08 02:50:22 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:22     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:22 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:22 <> Tuning RadialSVM by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:22 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:22 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:22 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:22 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:23 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        cost: {1, 10} => 10
  2026-10-08 02:50:23 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:23 Training RadialSVM Classification with tuned hyperparameters... [train]
  2026-10-08 02:50:23 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:23 One hot encoding gn... ✔ [one_hot]
  2026-10-08 02:50:23 Preprocessing done. [preprocess]
  
  <Classification>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           1
           versicolor          2          43
  
                       Overall  
          Sensitivity  0.978  
          Specificity  0.956  
    Balanced Accuracy  0.967  
                  Ppv  0.957  
                  Npv  0.977  
                   F1  0.967  
             Accuracy  0.967  
                  Auc  0.999  
          Brier Score  0.022  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.049  
  
       Positive Class virginica
  
  2026-10-08 02:50:23 ✓ Done in 1.14 seconds. [train]
  2026-10-08 02:50:23 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:23 ▶ [train]
  2026-10-08 02:50:23 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:23 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:23 <> Training RadialSVM Classification using 3 independent folds... [train]
  2026-10-08 02:50:23 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:23 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:23 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         93           7
           versicolor          4          96
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.930 (0.018)
          Specificity: 0.960 (0.045)
    Balanced Accuracy: 0.945 (0.016)
                  Ppv: 0.961 (0.043)
                  Npv: 0.932 (0.014)
                   F1: 0.945 (0.015)
             Accuracy: 0.945 (0.016)
                  Auc: 0.992 (4.2e-03)
          Brier Score: 0.044 (0.008)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           5
           versicolor          4          46
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.901 (0.067)
          Specificity: 0.920 (0.033)
    Balanced Accuracy: 0.911 (0.028)
                  Ppv: 0.920 (0.027)
                  Npv: 0.906 (0.055)
                   F1: 0.909 (0.032)
             Accuracy: 0.911 (0.028)
                  Auc: 0.979 (0.021)
          Brier Score: 0.056 (0.020)
  
  2026-10-08 02:50:24 ✓ Done in 0.36 seconds. [train]
  2026-10-08 02:50:24 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:24 ▶ [train]
  2026-10-08 02:50:24 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:24 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:50:24 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:24 <> Training RadialSVM Classification using 3 independent folds... [train]
  2026-10-08 02:50:24 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:24 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:27 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  RadialSVM (Support Vector Machine with Radial Kernel)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         97           3
           versicolor          2          98
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.970 (5.1e-04)
          Specificity: 0.980 (0.017)
    Balanced Accuracy: 0.975 (0.009)
                  Ppv: 0.980 (0.017)
                  Npv: 0.970 (1e-03)
                   F1: 0.975 (0.009)
             Accuracy: 0.975 (0.009)
                  Auc: 0.998 (1.4e-03)
          Brier Score: 0.024 (0.006)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         47           3
           versicolor          3          47
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.941 (0.102)
          Specificity: 0.939 (0.063)
    Balanced Accuracy: 0.940 (0.029)
                  Ppv: 0.944 (0.056)
                  Npv: 0.950 (0.087)
                   F1: 0.939 (0.034)
             Accuracy: 0.940 (0.029)
                  Auc: 0.989 (0.007)
          Brier Score: 0.048 (0.007)
  
  2026-10-08 02:50:27 ✓ Done in 3.05 seconds. [train]
  2026-10-08 02:50:27 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:27 ▶ [train]
  2026-10-08 02:50:27 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:27     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:27 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:27 Training RadialSVM Classification... [train]
  2026-10-08 02:50:27 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  RadialSVM (Support Vector Machine with Radial Kernel)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          39          6
            virginica       0           6         39
  
                       Overall  
    Balanced Accuracy  0.911  
                   F1  0.911  
             Accuracy  0.911  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.867       0.867    
          Specificity  1.000   0.933       0.933    
    Balanced Accuracy  1.000   0.900       0.900    
                  Ppv  1.000   0.867       0.867    
                  Npv  1.000   0.933       0.933    
                   F1  1.000   0.867       0.867    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           2          3
  
                       Overall  
    Balanced Accuracy  0.867  
                   F1  0.861  
             Accuracy  0.867  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       0.600    
          Specificity  1.000   0.800       1.000    
    Balanced Accuracy  1.000   0.900       0.800    
                  Ppv  1.000   0.714       1.000    
                  Npv  1.000   1.000       0.833    
                   F1  1.000   0.833       0.750    
  
  2026-10-08 02:50:27 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:50:27 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:27 ▶ [train]
  2026-10-08 02:50:27 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:27     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:27 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:27 Training CART Regression... [train]
  2026-10-08 02:50:27 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  CART (Classification and Regression Trees)
  
    <Training Regression Metrics>
     MAE: 0.84
     MSE: 1.13
    RMSE: 1.06
      R²: 0.77
  
    <Test Regression Metrics>
     MAE: 1.08
     MSE: 1.80
    RMSE: 1.34
      R²: 0.60
  
  2026-10-08 02:50:27 ✓ Done in 0.13 seconds. [train]
  2026-10-08 02:50:27 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:27 ▶ [train]
  2026-10-08 02:50:27 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:27     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:27 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:27 <> Tuning CART by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:27 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:27 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:27 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:28 Best config to minimize mse: [tune_GridSearch]
                        maxdepth: {2, 3} => 3
  2026-10-08 02:50:28 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:28 Training CART Regression with tuned hyperparameters... [train]
  2026-10-08 02:50:28 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.93
     MSE: 1.43
    RMSE: 1.19
      R²: 0.71
  
    <Test Regression Metrics>
     MAE: 1.05
     MSE: 1.59
    RMSE: 1.26
      R²: 0.64
  
  2026-10-08 02:50:28 ✓ Done in 0.99 seconds. [train]
  2026-10-08 02:50:28 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:28 ▶ [train]
  2026-10-08 02:50:28 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:28 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:28 <> Training CART Regression using 3 independent folds... [train]
  2026-10-08 02:50:28 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:28 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  CART (Classification and Regression Trees)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.788 (0.043)
     MSE: 0.981 (0.063)
    RMSE: 0.990 (0.032)
      R²: 0.800 (0.011)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.045 (0.051)
     MSE: 1.710 (0.156)
    RMSE: 1.307 (0.059)
      R²: 0.651 (0.033)
  
  2026-10-08 02:50:28 ✓ Done in 0.32 seconds. [train]
  2026-10-08 02:50:28 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:28 ▶ [train]
  2026-10-08 02:50:28 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:28 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:50:28 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:28 <> Training CART Regression using 3 independent folds... [train]
  2026-10-08 02:50:28 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:34 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.134 (0.054)
     MSE: 1.944 (0.122)
    RMSE: 1.394 (0.044)
      R²: 0.604 (0.021)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.223 (0.050)
     MSE: 2.403 (0.145)
    RMSE: 1.550 (0.047)
      R²: 0.510 (0.025)
  
  2026-10-08 02:50:34 ✓ Done in 5.78 seconds. [train]
  2026-10-08 02:50:34 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:34 ▶ [train]
  2026-10-08 02:50:34 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:34 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:50:34 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:34 <> Training CART Regression using 3 independent folds... [train]
  2026-10-08 02:50:34 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:37 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.804 (0.012)
     MSE: 1.022 (0.027)
    RMSE: 1.011 (0.013)
      R²: 0.791 (0.011)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.070 (0.022)
     MSE: 1.810 (0.049)
    RMSE: 1.345 (0.018)
      R²: 0.629 (0.028)
  
  2026-10-08 02:50:37 ✓ Done in 2.93 seconds. [train]
  2026-10-08 02:50:37 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:37 ▶ [train]
  2026-10-08 02:50:37 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:37     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:37 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: c(`_R_CHECK_LIMIT_CORES_` = 1); Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:37 <> Tuning CART by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:37 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:37 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:37 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:37 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:38 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        maxdepth: {1, 2} => 2
  2026-10-08 02:50:38 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:38 Training CART Classification with tuned hyperparameters... [train]
  2026-10-08 02:50:38 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.987  
          Brier Score  0.020  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.096  
  
       Positive Class virginica
  
  2026-10-08 02:50:38 ✓ Done in 0.97 seconds. [train]
  2026-10-08 02:50:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:38 ▶ [train]
  2026-10-08 02:50:38 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:38     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:38 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:38 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:50:38 Training CART Classification... [train]
  2026-10-08 02:50:38 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  CART (Classification and Regression Trees)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          0          45
  
                       Overall  
          Sensitivity  1.000  
          Specificity  1.000  
    Balanced Accuracy  1.000  
                  Ppv  1.000  
                  Npv  1.000  
                   F1  1.000  
             Accuracy  1.000  
                  Auc  1.000  
          Brier Score  0.000  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.100  
  
       Positive Class virginica
  
  2026-10-08 02:50:38 ✓ Done in 0.13 seconds. [train]
  2026-10-08 02:50:38 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:38 ▶ [train]
  2026-10-08 02:50:38 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:38     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:38 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:38 <> Tuning CART by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:38 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:38 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:38 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:38 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:39 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        maxdepth: {1, 2} => 2
  2026-10-08 02:50:39 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:39 Training CART Classification with tuned hyperparameters... [train]
  2026-10-08 02:50:39 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.987  
          Brier Score  0.020  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.096  
  
       Positive Class virginica
  
  2026-10-08 02:50:39 ✓ Done in 1.02 seconds. [train]
  2026-10-08 02:50:39 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:39 ▶ [train]
  2026-10-08 02:50:39 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:39 Tuning parallelization enabled. [get_n_workers]
  2026-10-08 02:50:39 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:39 <> Training CART Classification using 3 independent folds... [train]
  2026-10-08 02:50:39 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:39 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:42 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         94           6
           versicolor          5          95
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.941 (0.059)
          Specificity: 0.949 (0.063)
    Balanced Accuracy: 0.945 (0.008)
                  Ppv: 0.954 (0.056)
                  Npv: 0.945 (0.053)
                   F1: 0.945 (0.008)
             Accuracy: 0.945 (0.008)
                  Auc: 0.946 (0.008)
          Brier Score: 0.050 (0.006)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         46           4
           versicolor          4          46
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.920 (0.089)
          Specificity: 0.920 (0.089)
    Balanced Accuracy: 0.920 (0.015)
                  Ppv: 0.929 (0.075)
                  Npv: 0.929 (0.075)
                   F1: 0.920 (0.017)
             Accuracy: 0.920 (0.015)
                  Auc: 0.911 (0.028)
          Brier Score: 0.075 (0.011)
  
  2026-10-08 02:50:43 ✓ Done in 3.24 seconds. [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 ▶ [train]
  2026-10-08 02:50:43 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:43     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:43 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:43 Training CART Classification... [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  CART (Classification and Regression Trees)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          44          1
            virginica       0           0         45
  
                       Overall  
    Balanced Accuracy  0.993  
                   F1  0.993  
             Accuracy  0.993  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.978       1.000    
          Specificity  1.000   1.000       0.989    
    Balanced Accuracy  1.000   0.989       0.994    
                  Ppv  1.000   1.000       0.978    
                  Npv  1.000   0.989       1.000    
                   F1  1.000   0.989       0.989    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           0          5
  
                       Overall  
    Balanced Accuracy  1.000  
                   F1  1.000  
             Accuracy  1.000  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       1.000    
          Specificity  1.000   1.000       1.000    
    Balanced Accuracy  1.000   1.000       1.000    
                  Ppv  1.000   1.000       1.000    
                  Npv  1.000   1.000       1.000    
                   F1  1.000   1.000       1.000    
  
  2026-10-08 02:50:43 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 ▶ [train]
  2026-10-08 02:50:43 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:43     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:43 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:43 Training LightCART Regression... [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:43 Preprocessing done. [preprocess]
  
  <Regression>
  LightCART (Decision Tree)
  
    <Training Regression Metrics>
     MAE: 1.66
     MSE: 4.24
    RMSE: 2.06
      R²: 0.15
  
    <Test Regression Metrics>
     MAE: 1.58
     MSE: 3.75
    RMSE: 1.94
      R²: 0.16
  
  2026-10-08 02:50:43 ✓ Done in 0.27 seconds. [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 ▶ [train]
  2026-10-08 02:50:43 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:43     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:43 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:43 Training LightCART Regression... [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:43 Preprocessing done. [preprocess]
  
  <Regression>
  LightCART (Decision Tree)
  
    <Training Regression Metrics>
     MAE: 1.66
     MSE: 4.24
    RMSE: 2.06
      R²: 0.15
  
    <Test Regression Metrics>
     MAE: 1.58
     MSE: 3.75
    RMSE: 1.94
      R²: 0.16
  
  2026-10-08 02:50:43 ✓ Done in 0.20 seconds. [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 ▶ [train]
  2026-10-08 02:50:43 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:43     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:43 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:43 Training LightCART Classification... [train]
  2026-10-08 02:50:43 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:43 Converting 2 factors to integer... [preprocess]
  2026-10-08 02:50:43 Preprocessing done. [preprocess]
  
  <Classification>
  LightCART (Decision Tree)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         41           4
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.911  
          Specificity  0.978  
    Balanced Accuracy  0.944  
                  Ppv  0.976  
                  Npv  0.917  
                   F1  0.943  
             Accuracy  0.944  
                  Auc  0.978  
          Brier Score  0.211  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.860  
          Brier Score  0.219  
  
       Positive Class virginica
  
  2026-10-08 02:50:43 ✓ Done in 0.19 seconds. [train]
  2026-10-08 02:50:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:44 ▶ [train]
  2026-10-08 02:50:44 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:44     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:44 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:44 Training LightCART Classification... [train]
  2026-10-08 02:50:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:44 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:44 Preprocessing done. [preprocess]
  
  <Classification>
  LightCART (Decision Tree)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      44           1          0
           versicolor       0          43          2
            virginica       0           3         42
  
                       Overall  
    Balanced Accuracy  0.956  
                   F1  0.956  
             Accuracy  0.956  
                       Setosa  Versicolor  Virginica  
          Sensitivity  0.978   0.956       0.933    
          Specificity  1.000   0.956       0.978    
    Balanced Accuracy  0.989   0.956       0.956    
                  Ppv  1.000   0.915       0.955    
                  Npv  0.989   0.977       0.967    
                   F1  0.989   0.935       0.944    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           1          4
  
                       Overall  
    Balanced Accuracy  0.933  
                   F1  0.933  
             Accuracy  0.933  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       0.800    
          Specificity  1.000   0.900       1.000    
    Balanced Accuracy  1.000   0.950       0.900    
                  Ppv  1.000   0.833       1.000    
                  Npv  1.000   1.000       0.909    
                   F1  1.000   0.909       0.889    
  
  2026-10-08 02:50:44 ✓ Done in 0.19 seconds. [train]
  2026-10-08 02:50:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:44 ▶ [train]
  2026-10-08 02:50:44 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:44     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:44 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:44 Training LightRF Regression... [train]
  2026-10-08 02:50:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:44 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:44 Preprocessing done. [preprocess]
  
  <Regression>
  LightRF (LightGBM Random Forest)
  
    <Training Regression Metrics>
     MAE: 0.92
     MSE: 1.34
    RMSE: 1.16
      R²: 0.73
  
    <Test Regression Metrics>
     MAE: 0.89
     MSE: 1.18
    RMSE: 1.09
      R²: 0.73
  
  2026-10-08 02:50:44 ✓ Done in 0.49 seconds. [train]
  2026-10-08 02:50:44 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:44 Preprocessing done. [preprocess]
  2026-10-08 02:50:44 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:44 ▶ [train]
  2026-10-08 02:50:44 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:44     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:44 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:44 <> Tuning LightRF by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:44 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:44 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:44 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:47 Best config to minimize mse: [tune_GridSearch]
                        lambda_l1: {0, 0.1} => 0
  2026-10-08 02:50:47 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:47 Training LightRF Regression with tuned hyperparameters... [train]
  2026-10-08 02:50:47 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:47 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:47 Preprocessing done. [preprocess]
  
  <Regression>
  LightRF (LightGBM Random Forest)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.92
     MSE: 1.33
    RMSE: 1.15
      R²: 0.73
  
    <Test Regression Metrics>
     MAE: 0.88
     MSE: 1.17
    RMSE: 1.08
      R²: 0.74
  
  2026-10-08 02:50:47 ✓ Done in 2.79 seconds. [train]
  2026-10-08 02:50:47 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:47 ▶ [train]
  2026-10-08 02:50:47 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:47 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:47 <> Training LightRF Regression using 3 independent folds... [train]
  2026-10-08 02:50:47 Input contains more than one column; stratifying on last. [resample]
  \ 9/10 ETA:  0s | Tuning... (10 combinations) 
  2026-10-08 02:50:55 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  LightRF (LightGBM Random Forest)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.962 (0.025)
     MSE: 1.446 (0.126)
    RMSE: 1.202 (0.053)
      R²: 0.705 (0.030)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.035 (0.067)
     MSE: 1.657 (0.266)
    RMSE: 1.284 (0.103)
      R²: 0.661 (0.064)
  
  2026-10-08 02:50:55 ✓ Done in 7.80 seconds. [train]
  2026-10-08 02:50:55 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:55 ▶ [train]
  2026-10-08 02:50:55 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:55     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:55 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:55 Training LightRF Classification... [train]
  2026-10-08 02:50:55 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:55 Converting 2 factors to integer... [preprocess]
  2026-10-08 02:50:55 Preprocessing done. [preprocess]
  
  <Classification>
  LightRF (LightGBM Random Forest)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         41           4
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.911  
          Specificity  0.978  
    Balanced Accuracy  0.944  
                  Ppv  0.976  
                  Npv  0.917  
                   F1  0.943  
             Accuracy  0.944  
                  Auc  0.996  
          Brier Score  0.048  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.092  
  
       Positive Class virginica
  
  2026-10-08 02:50:55 ✓ Done in 0.28 seconds. [train]
  2026-10-08 02:50:55 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:55 Preprocessing done. [preprocess]
  2026-10-08 02:50:55 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:55 ▶ [train]
  2026-10-08 02:50:55 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:55     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:55 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:55 <> Tuning LightRF by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:55 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:50:55 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:55 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:55 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:50:57 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        max_depth: {-1, 5} => -1
  2026-10-08 02:50:57 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:50:57 Training LightRF Classification with tuned hyperparameters... [train]
  2026-10-08 02:50:57 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:57 Converting 2 factors to integer... [preprocess]
  2026-10-08 02:50:57 Preprocessing done. [preprocess]
  
  <Classification>
  LightRF (LightGBM Random Forest)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         41           4
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.911  
          Specificity  0.978  
    Balanced Accuracy  0.944  
                  Ppv  0.976  
                  Npv  0.917  
                   F1  0.943  
             Accuracy  0.944  
                  Auc  0.996  
          Brier Score  0.048  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.092  
  
       Positive Class virginica
  
  2026-10-08 02:50:57 ✓ Done in 2.19 seconds. [train]
  2026-10-08 02:50:57 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:57 ▶ [train]
  2026-10-08 02:50:57 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:50:57 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:57 <> Training LightRF Classification using 3 independent folds... [train]
  2026-10-08 02:50:57 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:57 Using max n bins possible = 2. [kfold]
  2026-10-08 02:50:58 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  LightRF (LightGBM Random Forest)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         95           5
           versicolor          4          96
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.950 (0.018)
          Specificity: 0.960 (0.045)
    Balanced Accuracy: 0.955 (0.014)
                  Ppv: 0.962 (0.042)
                  Npv: 0.951 (0.015)
                   F1: 0.955 (0.013)
             Accuracy: 0.955 (0.014)
                  Auc: 0.988 (0.013)
          Brier Score: 0.111 (0.022)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           5
           versicolor          3          47
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.901 (0.067)
          Specificity: 0.941 (0.102)
    Balanced Accuracy: 0.921 (0.044)
                  Ppv: 0.947 (0.091)
                  Npv: 0.908 (0.051)
                   F1: 0.920 (0.042)
             Accuracy: 0.921 (0.044)
                  Auc: 0.976 (0.030)
          Brier Score: 0.115 (0.038)
  
  2026-10-08 02:50:58 ✓ Done in 0.92 seconds. [train]
  2026-10-08 02:50:58 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:58 ▶ [train]
  2026-10-08 02:50:58 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:58     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:50:58 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:58 Training LightRF Classification... [train]
  2026-10-08 02:50:58 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:58 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:58 Preprocessing done. [preprocess]
  
  <Classification>
  LightRF (LightGBM Random Forest)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       1          42          2
            virginica       0           3         42
  
                       Overall  
    Balanced Accuracy  0.956  
                   F1  0.955  
             Accuracy  0.956  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.933       0.933    
          Specificity  0.989   0.967       0.978    
    Balanced Accuracy  0.994   0.950       0.956    
                  Ppv  0.978   0.933       0.955    
                  Npv  1.000   0.967       0.967    
                   F1  0.989   0.933       0.944    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           1          4
  
                       Overall  
    Balanced Accuracy  0.933  
                   F1  0.933  
             Accuracy  0.933  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       0.800    
          Specificity  1.000   0.900       1.000    
    Balanced Accuracy  1.000   0.950       0.900    
                  Ppv  1.000   0.833       1.000    
                  Npv  1.000   1.000       0.909    
                   F1  1.000   0.909       0.889    
  
  2026-10-08 02:50:59 ✓ Done in 0.43 seconds. [train]
  2026-10-08 02:50:59 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:59 ▶ [train]
  2026-10-08 02:50:59 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:59     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:59 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:59 Training LightGBM Regression... [train]
  2026-10-08 02:50:59 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:59 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:50:59 Preprocessing done. [preprocess]
  
  <Regression>
  LightGBM (Gradient Boosting)
  
    <Training Regression Metrics>
     MAE: 1.29
     MSE: 2.63
    RMSE: 1.62
      R²: 0.47
  
    <Test Regression Metrics>
     MAE: 1.20
     MSE: 2.33
    RMSE: 1.53
      R²: 0.48
  
  2026-10-08 02:50:59 ✓ Done in 0.36 seconds. [train]
  2026-10-08 02:50:59 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:50:59 ▶ [train]
  2026-10-08 02:50:59 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:59     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:50:59 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:50:59 <> Tuning LightGBM by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:50:59 1 parameter combination x 5 resamples: 5 models total (). [tune_GridSearch]
  2026-10-08 02:50:59 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:50:59 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:51:03 Best config to minimize mse: [tune_GridSearch]
                        nrounds: {} => 434
  2026-10-08 02:51:03 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:51:03 Training LightGBM Regression with tuned hyperparameters... [train]
  2026-10-08 02:51:03 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:03 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:51:03 Preprocessing done. [preprocess]
  
  <Regression>
  LightGBM (Gradient Boosting)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.60
     MSE: 0.58
    RMSE: 0.76
      R²: 0.88
  
    <Test Regression Metrics>
     MAE: 0.80
     MSE: 1.19
    RMSE: 1.09
      R²: 0.73
  
  2026-10-08 02:51:06 ✓ Done in 6.75 seconds. [train]
  2026-10-08 02:51:06 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:06 ▶ [train]
  2026-10-08 02:51:06 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:06 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:06 <> Training LightGBM Regression using 3 independent folds... [train]
  2026-10-08 02:51:06 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:10 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  LightGBM (Gradient Boosting)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.287 (0.027)
     MSE: 2.609 (0.113)
    RMSE: 1.615 (0.035)
      R²: 0.473 (0.006)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 1.336 (0.047)
     MSE: 2.820 (0.253)
    RMSE: 1.678 (0.075)
      R²: 0.429 (0.020)
  
  2026-10-08 02:51:11 ✓ Done in 4.50 seconds. [train]
  2026-10-08 02:51:11 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:11 ▶ [train]
  2026-10-08 02:51:11 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:11     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:11 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:11 <> Tuning LightGBM by exhaustive grid search with 3 independent folds... [tune_GridSearch]
  2026-10-08 02:51:11 1 parameter combination x 3 resamples: 3 models total (). [tune_GridSearch]
  2026-10-08 02:51:11 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:11 Using max n bins possible = 2. [kfold]
  2026-10-08 02:51:11 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:51:12 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        nrounds: {} => 375
  2026-10-08 02:51:12 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:51:12 Training LightGBM Classification with tuned hyperparameters... [train]
  2026-10-08 02:51:12 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:12 Converting 2 factors to integer... [preprocess]
  2026-10-08 02:51:12 Preprocessing done. [preprocess]
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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  <Classification>
  LightGBM (Gradient Boosting)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.999  
          Brier Score  0.017  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.097  
  
       Positive Class virginica
  
  2026-10-08 02:51:13 ✓ Done in 2.70 seconds. [train]
  2026-10-08 02:51:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:13 ▶ [train]
  2026-10-08 02:51:13 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:51:13     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:51:13 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:13 Training LightGBM Classification... [train]
  2026-10-08 02:51:13 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:13 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:51:13 Preprocessing done. [preprocess]
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  
  <Classification>
  LightGBM (Gradient Boosting)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          43          2
            virginica       0           3         42
  
                       Overall  
    Balanced Accuracy  0.963  
                   F1  0.963  
             Accuracy  0.963  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.956       0.933    
          Specificity  1.000   0.967       0.978    
    Balanced Accuracy  1.000   0.961       0.956    
                  Ppv  1.000   0.935       0.955    
                  Npv  1.000   0.978       0.967    
                   F1  1.000   0.945       0.944    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           1          4
  
                       Overall  
    Balanced Accuracy  0.933  
                   F1  0.933  
             Accuracy  0.933  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       0.800    
          Specificity  1.000   0.900       1.000    
    Balanced Accuracy  1.000   0.950       0.900    
                  Ppv  1.000   0.833       1.000    
                  Npv  1.000   1.000       0.909    
                   F1  1.000   0.909       0.889    
  
  2026-10-08 02:51:14 ✓ Done in 0.42 seconds. [train]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:14 ▶ [train]
  2026-10-08 02:51:14 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:14     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:14 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:14 Training LightRuleFit Regression... [train]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:14 ▶ [train]
  2026-10-08 02:51:14 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:14 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:14 Training LightGBM Regression... [train]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:14 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:51:14 Preprocessing done. [preprocess]
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  
  <Regression>
  LightGBM (Gradient Boosting)
  
    <Training Regression Metrics>
     MAE: 0.78
     MSE: 0.96
    RMSE: 0.98
      R²: 0.81
  
  2026-10-08 02:51:14 ✓ Done in 0.29 seconds. [train]
  2026-10-08 02:51:14 Extracting LightGBM rules... ✔ [extract_rules]
  2026-10-08 02:51:14 Extracted 180 unique rules. [extract_rules]
  2026-10-08 02:51:14 Matching180rules to358cases... ✔ [match_cases_by_rules]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:14 ▶ [train]
  2026-10-08 02:51:14 Training set: 358 cases x 180 features. [summarize_supervised]
  2026-10-08 02:51:14 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:14 Training GLMNET Regression... [train]
  2026-10-08 02:51:14 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLMNET (Elastic Net)
  
    <Training Regression Metrics>
     MAE: 0.64
     MSE: 0.65
    RMSE: 0.80
      R²: 0.87
  
  2026-10-08 02:51:15 ✓ Done in 0.18 seconds. [train]
  
  <Regression>
  LightRuleFit (LightGBM RuleFit)
  
    <Training Regression Metrics>
     MAE: 0.64
     MSE: 0.65
    RMSE: 0.80
      R²: 0.87
  
    <Test Regression Metrics>
     MAE: 0.82
     MSE: 1.25
    RMSE: 1.12
      R²: 0.72
  
  2026-10-08 02:51:15 ✓ Done in 1.34 seconds. [train]
  2026-10-08 02:51:15 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:15 ▶ [train]
  2026-10-08 02:51:15 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:15     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:15 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:15 Training LightRuleFit Classification... [train]
  2026-10-08 02:51:15 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:15 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:15 ▶ [train]
  2026-10-08 02:51:15 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:15 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:15 Training LightGBM Classification... [train]
  2026-10-08 02:51:15 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:15 Converting 2 factors to integer... [preprocess]
  2026-10-08 02:51:15 Preprocessing done. [preprocess]
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  
  <Classification>
  LightGBM (Gradient Boosting)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.998  
          Brier Score  0.019  
  
       Positive Class virginica
  
  2026-10-08 02:51:16 ✓ Done in 0.32 seconds. [train]
  2026-10-08 02:51:16 Extracting LightGBM rules... ✔ [extract_rules]
  2026-10-08 02:51:16 Extracted 12 unique rules. [extract_rules]
  2026-10-08 02:51:16 Matching12rules to90cases... ✔ [match_cases_by_rules]
  2026-10-08 02:51:16 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:16 ▶ [train]
  2026-10-08 02:51:16 Training set: 90 cases x 12 features. [summarize_supervised]
  2026-10-08 02:51:16 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:16 <> Tuning GLMNET by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:51:16 1 parameter combination x 5 resamples: 5 models total (). [tune_GridSearch]
  2026-10-08 02:51:16 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:16 Using max n bins possible = 2. [kfold]
  2026-10-08 02:51:16 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:51:17 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        lambda: {} => 0.0658148865916772
  2026-10-08 02:51:17 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:51:17 Calculating case weights using Inverse Frequency Weighting. [ifw]
  2026-10-08 02:51:17 Training GLMNET Classification with tuned hyperparameters... [train]
  2026-10-08 02:51:17 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLMNET (Elastic Net)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.996  
          Brier Score  0.028  
  
       Positive Class virginica
  
  2026-10-08 02:51:17 ✓ Done in 1.50 seconds. [train]
  
  <Classification>
  LightRuleFit (LightGBM RuleFit)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.996  
          Brier Score  0.028  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.094  
  
       Positive Class virginica
  
  2026-10-08 02:51:17 ✓ Done in 2.12 seconds. [train]
  2026-10-08 02:51:19 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:19 ▶ [train]
  2026-10-08 02:51:19 Training set: 50 cases x 1 features. [summarize_supervised]
  2026-10-08 02:51:19 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:19 Training Isotonic Regression... [train]
  2026-10-08 02:51:19 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  Isotonic (Isotonic Regression)
  
    <Training Regression Metrics>
     MAE: 0.51
     MSE: 0.61
    RMSE: 0.78
      R²: 0.99
  
  2026-10-08 02:51:19 ✓ Done in 0.09 seconds. [train]
  2026-10-08 02:51:19 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:19 ▶ [train]
  2026-10-08 02:51:19 Training set: 200 cases x 1 features. [summarize_supervised]
  2026-10-08 02:51:19 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:19 Training Isotonic Classification... [train]
  2026-10-08 02:51:19 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  Isotonic (Isotonic Regression)
  
    <Training Classification Metrics>
                       Predicted
            Reference  b   a   
                    b  90   6
                    a  12  92
  
                       Overall  
          Sensitivity  0.938  
          Specificity  0.885  
    Balanced Accuracy  0.911  
                  Ppv  0.882  
                  Npv  0.939  
                   F1  0.909  
             Accuracy  0.910  
                  Auc  0.978  
          Brier Score  0.057  
  
       Positive Class b
  
  2026-10-08 02:51:19 ✓ Done in 0.11 seconds. [train]
  2026-10-08 02:51:19 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:19 ▶ [train]
  2026-10-08 02:51:19 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:20     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:20 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:20 Training Ranger Regression... [train]
  2026-10-08 02:51:20 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  Ranger (Random Forest)
  
    <Training Regression Metrics>
     MAE: 0.40
     MSE: 0.26
    RMSE: 0.51
      R²: 0.95
  
    <Test Regression Metrics>
     MAE: 0.89
     MSE: 1.29
    RMSE: 1.13
      R²: 0.71
  
  2026-10-08 02:51:20 ✓ Done in 0.18 seconds. [train]
  2026-10-08 02:51:20 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:20 ▶ [train]
  2026-10-08 02:51:20 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:20     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:20 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:20 <> Tuning Ranger by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:51:20 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:51:20 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:20 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:51:22 Best config to minimize mse: [tune_GridSearch]
                        mtry: {3, 6} => 6
  2026-10-08 02:51:22 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:51:22 Training Ranger Regression with tuned hyperparameters... [train]
  2026-10-08 02:51:22 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  Ranger (Random Forest)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Regression Metrics>
     MAE: 0.34
     MSE: 0.20
    RMSE: 0.44
      R²: 0.96
  
    <Test Regression Metrics>
     MAE: 0.91
     MSE: 1.38
    RMSE: 1.18
      R²: 0.69
  
  2026-10-08 02:51:22 ✓ Done in 2.36 seconds. [train]
  2026-10-08 02:51:22 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:22 ▶ [train]
  2026-10-08 02:51:22 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:22 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:22 <> Training Ranger Regression using 3 independent folds... [train]
  2026-10-08 02:51:22 Input contains more than one column; stratifying on last. [resample]
  \ 1/3 ETA:  5s | Training outer resamples... 
  2026-10-08 02:51:30 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  Ranger (Random Forest)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.412 (3.1e-03)
     MSE: 0.264 (4.9e-03)
    RMSE: 0.514 (4.8e-03)
      R²: 0.946 (1.7e-03)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.878 (3.4e-03)
     MSE: 1.188 (0.009)
    RMSE: 1.090 (4e-03)
      R²: 0.758 (0.007)
  
  2026-10-08 02:51:30 ✓ Done in 8.20 seconds. [train]
  2026-10-08 02:51:30 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:30 ▶ [train]
  2026-10-08 02:51:30 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:30     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:30 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:30 Training Ranger Classification... [train]
  2026-10-08 02:51:30 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  Ranger (Random Forest)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           1
           versicolor          2          43
  
                       Overall  
          Sensitivity  0.978  
          Specificity  0.956  
    Balanced Accuracy  0.967  
                  Ppv  0.957  
                  Npv  0.977  
                   F1  0.967  
             Accuracy  0.967  
                  Auc  0.998  
          Brier Score  0.024  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.071  
  
       Positive Class virginica
  
  2026-10-08 02:51:30 ✓ Done in 0.13 seconds. [train]
  2026-10-08 02:51:30 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:30 ▶ [train]
  2026-10-08 02:51:30 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:30     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:30 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:30 <> Tuning Ranger by exhaustive grid search with 5 independent folds... [tune_GridSearch]
  2026-10-08 02:51:30 2 parameter combinations x 5 resamples: 10 models total (). [tune_GridSearch]
  2026-10-08 02:51:31 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:31 Using max n bins possible = 2. [kfold]
  2026-10-08 02:51:31 Tuning in sequence [tune_GridSearch]
  2026-10-08 02:51:31 Best config to maximize balanced_accuracy: [tune_GridSearch]
                        mtry: {2, 4} => 4
  2026-10-08 02:51:31 </> Tuning done. [tune_GridSearch]
  2026-10-08 02:51:31 Training Ranger Classification with tuned hyperparameters... [train]
  2026-10-08 02:51:31 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  Ranger (Random Forest)
  ⚙ Tuned using exhaustive grid search.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         43           2
           versicolor          2          43
  
                       Overall  
          Sensitivity  0.956  
          Specificity  0.956  
    Balanced Accuracy  0.956  
                  Ppv  0.956  
                  Npv  0.956  
                   F1  0.956  
             Accuracy  0.956  
                  Auc  0.997  
          Brier Score  0.025  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.051  
  
       Positive Class virginica
  
  2026-10-08 02:51:32 ✓ Done in 1.12 seconds. [train]
  2026-10-08 02:51:32 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:32 ▶ [train]
  2026-10-08 02:51:32 Training set: 100 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:32 // Max workers: 1 { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:32 <> Training Ranger Classification using 3 independent folds... [train]
  2026-10-08 02:51:32 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:32 Using max n bins possible = 2. [kfold]
  2026-10-08 02:51:32 </> Outer resampling done. [train]
  
  <Resampled Classification Model>
  Ranger (Random Forest)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Classification Training Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         97           3
           versicolor          3          97
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.970 (0.029)
          Specificity: 0.970 (0.029)
    Balanced Accuracy: 0.970 (0.025)
                  Ppv: 0.971 (0.029)
                  Npv: 0.971 (0.029)
                   F1: 0.970 (0.025)
             Accuracy: 0.970 (0.025)
                  Auc: 0.997 (2.7e-03)
          Brier Score: 0.021 (0.011)
  
    <Resampled Classification Test Metrics>
    Aggregate Confusion Matrix across resamples.
                       Predicted
            Reference  virginica  versicolor  
            virginica         46           4
           versicolor          3          47
  
    Showing mean (sd) across resamples.
          Sensitivity: 0.920 (0.089)
          Specificity: 0.940 (2.1e-03)
    Balanced Accuracy: 0.930 (0.045)
                  Ppv: 0.938 (0.006)
                  Npv: 0.927 (0.080)
                   F1: 0.928 (0.049)
             Accuracy: 0.930 (0.045)
                  Auc: 0.984 (9.8e-04)
          Brier Score: 0.065 (0.027)
  
  2026-10-08 02:51:32 ✓ Done in 0.41 seconds. [train]
  2026-10-08 02:51:32 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:32 ▶ [train]
  2026-10-08 02:51:32 Training set: 135 cases x 4 features. [summarize_supervised]
  2026-10-08 02:51:32     Test set: 15 cases x 4 features. [summarize_supervised]
  2026-10-08 02:51:32 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: c(`_R_CHECK_LIMIT_CORES_` = 1); Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:32 Training Ranger Classification... [train]
  2026-10-08 02:51:32 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  Ranger (Random Forest)
  
    <Training Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa      45           0          0
           versicolor       0          42          3
            virginica       0           2         43
  
                       Overall  
    Balanced Accuracy  0.963  
                   F1  0.963  
             Accuracy  0.963  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   0.933       0.956    
          Specificity  1.000   0.978       0.967    
    Balanced Accuracy  1.000   0.956       0.961    
                  Ppv  1.000   0.955       0.935    
                  Npv  1.000   0.967       0.978    
                   F1  1.000   0.944       0.945    
  
    <Test Classification Metrics>
                       Predicted
            Reference  setosa  versicolor  virginica  
               setosa       5           0          0
           versicolor       0           5          0
            virginica       0           0          5
  
                       Overall  
    Balanced Accuracy  1.000  
                   F1  1.000  
             Accuracy  1.000  
                       Setosa  Versicolor  Virginica  
          Sensitivity  1.000   1.000       1.000    
          Specificity  1.000   1.000       1.000    
    Balanced Accuracy  1.000   1.000       1.000    
                  Ppv  1.000   1.000       1.000    
                  Npv  1.000   1.000       1.000    
                   F1  1.000   1.000       1.000    
  
  2026-10-08 02:51:32 ✓ Done in 0.14 seconds. [train]
  2026-10-08 02:51:32 <> Calibrating LightRF classification... [calibrate]
  2026-10-08 02:51:32 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:32 ▶ [train]
  2026-10-08 02:51:32 Training set: 90 cases x 1 features. [summarize_supervised]
  2026-10-08 02:51:32     Test set: 10 cases x 1 features. [summarize_supervised]
  2026-10-08 02:51:32 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:32 Training Isotonic Classification... [train]
  2026-10-08 02:51:32 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  Isotonic (Isotonic Regression)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         45           0
           versicolor          2          43
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.956  
    Balanced Accuracy  0.978  
                  Ppv  0.957  
                  Npv  1.000  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.997  
          Brier Score  0.017  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  0.900  
          Brier Score  0.100  
  
       Positive Class virginica
  
  2026-10-08 02:51:32 ✓ Done in 0.13 seconds. [train]
  
  <Classification>
  LightRF (LightGBM Random Forest)
  ⟋ Calibrated using Isotonic Regression.
  
    <Training Classification Metrics (Pre => Post Calibration)>
                       Predicted
            Reference  virginica  versicolor  
            virginica   41 => 45      4 => 0
           versicolor     1 => 2    44 => 43
  
                       Overall       
          Sensitivity  0.91 => 1.00
          Specificity  0.98 => 0.96
    Balanced Accuracy  0.94 => 0.98
                  Ppv  0.98 => 0.96
                  Npv  0.92 => 1.00
                   F1  0.94 => 0.98
             Accuracy  0.94 => 0.98
                  Auc  1.00 => 1.00
          Brier Score  0.05 => 0.02
  
       Positive Class virginica
  
    <Test Classification Metrics (Pre => Post Calibration)>
                       Predicted
            Reference  virginica  versicolor  
            virginica     5 => 5      0 => 0
           versicolor     1 => 1      4 => 4
  
                       Overall       
          Sensitivity  1.00 => 1.00
          Specificity  0.80 => 0.80
    Balanced Accuracy  0.90 => 0.90
                  Ppv  0.83 => 0.83
                  Npv  1.00 => 1.00
                   F1  0.91 => 0.91
             Accuracy  0.90 => 0.90
                  Auc  0.90 => 0.90
          Brier Score  0.09 => 0.10
  
       Positive Class virginica
  
  2026-10-08 02:51:33 </> Calibration done. [calibrate]
  2026-10-08 02:51:33 Converting 1 factor to integer... [preprocess]
  2026-10-08 02:51:33 Preprocessing done. [preprocess]
  2026-10-08 02:51:33 <> Calibrating LightRF resampled classification... [calibrate]
  
  <Resampled Classification Model>
  LightRF (LightGBM Random Forest)
  ⟳ Tested using 3 independent folds.
  ⟋ Calibrated using Isotonic Regression with 5 independent folds.
  
    <Resampled Classification Training Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.95 (0.02) => 1.00 (0.00)
          specificity: 0.96 (0.04) => 0.92 (0.08)
    balanced_accuracy: 0.96 (0.01) => 0.96 (0.04)
                  ppv: 0.96 (0.04) => 0.93 (0.07)
                  npv: 0.95 (0.02) => 1.00 (0.00)
                   f1: 0.96 (0.01) => 0.96 (0.04)
             accuracy: 0.96 (0.01) => 0.96 (0.04)
                  auc: 0.99 (0.01) => 0.98 (0.02)
          brier_score: 0.11 (0.02) => 0.03 (0.03)
  
    <Resampled Classification Test Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.90 (0.07) => 0.93 (0.19)
          specificity: 0.94 (0.10) => 0.85 (0.20)
    balanced_accuracy: 0.92 (0.04) => 0.89 (0.12)
                  ppv: 0.95 (0.09) => 0.89 (0.14)
                  npv: 0.91 (0.05) => 0.96 (0.12)
                   f1: 0.92 (0.04) => 0.89 (0.14)
             accuracy: 0.92 (0.04) => 0.89 (0.12)
                  auc: 0.98 (0.03) => 0.92 (0.11)
          brier_score: 0.12 (0.04) => 0.09 (0.11)
  
  2026-10-08 02:51:34 </> Calibration done. [calibrate]
  Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set. 
  Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set. 
  Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set. 
  Generalized Linear Model was used for regression. R-squared was 0.83 on the training set and 0.77 on the test set. 
  Generalized Linear Model was used for classification. Balanced accuracy was 0.98 in the training set and 0.90 in the test set. 
  Generalized Linear Model was used for regression. Mean R-squared was 0.83 on the training set and 0.82 on the test set across 3 independent folds. 
  Generalized Linear Model was used for classification. Mean balanced accuracy was 0.99 in the training set and 0.89 in the test set across 3 independent folds. 
  Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
  The top-performing model was GLM with a test-set Rsq of 0.817, followed by CART with rsq of 0.629 respectively. 
  Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Classification.
  The top-performing model was CART with a test-set Balanced Accuracy of 0.920, followed by GLM with balanced_accuracy of 0.888 respectively. 
  Generalized Linear Model (GLM) and Classification and Regression Trees (CART) were used for Regression.
  The top-performing model was GLM with a test-set Rsq of 0.770, followed by CART with rsq of 0.595 respectively. 
  2026-10-08 02:51:39 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:39 ▶ [train]
  2026-10-08 02:51:39 Training set: 358 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:39     Test set: 42 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:39 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:39 Training GLM Regression... [train]
  2026-10-08 02:51:39 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLM (Generalized Linear Model)
  
    <Training Regression Metrics>
     MAE: 0.73
     MSE: 0.82
    RMSE: 0.91
      R²: 0.83
  
    <Test Regression Metrics>
     MAE: 0.74
     MSE: 1.03
    RMSE: 1.01
      R²: 0.77
  
  2026-10-08 02:51:40 Writing data to D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\file36bb0595f3ceb/mod_r_glm...✔ 0.28 secs [rt_save]
  2026-10-08 02:51:40 Reload with: > obj <- readRDS('D:/temp/2026_10_07_16_17_17_27527/RtmpOQZuxw/file36bb0595f3ceb/mod_r_glm/train_GLM.rds') [rt_save]
  2026-10-08 02:51:40 ✓ Done in 0.43 seconds. [train]
  2026-10-08 02:51:40 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:40 ▶ [train]
  2026-10-08 02:51:40 Training set: 400 cases x 6 features. [summarize_supervised]
  2026-10-08 02:51:40 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:51:40 <> Training GLM Regression using 3 independent folds... [train]
  2026-10-08 02:51:40 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:51:40 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  GLM (Generalized Linear Model)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.723 (0.039)
     MSE: 0.831 (0.080)
    RMSE: 0.911 (0.044)
      R²: 0.830 (0.019)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.756 (0.096)
     MSE: 0.909 (0.188)
    RMSE: 0.950 (0.101)
      R²: 0.813 (0.046)
  
  2026-10-08 02:51:40 Writing data to D:\temp\2026_10_07_16_17_17_27527\RtmpOQZuxw\file36bb0c6de88/resmod_r_glm...✔ 0.65 secs [rt_save]
  2026-10-08 02:51:41 Reload with: > obj <- readRDS('D:/temp/2026_10_07_16_17_17_27527/RtmpOQZuxw/file36bb0c6de88/resmod_r_glm/train_GLM.rds') [rt_save]
  2026-10-08 02:51:41 ✓ Done in 1.03 seconds. [train]
  Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
  The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively. 
  Classification and Regression Trees (CART), LightGBM Random Forest (LightRF), and Gradient Boosting (LightGBM) were used for Classification.
  The top-performing model was CART with a test-set Balanced Accuracy of 0.900, followed by LightRF and LightGBM with balanced_accuracy of 0.900 and 0.900 respectively. 
  Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
  The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively. 
  Elastic Net (GLMNET), Support Vector Machine with Radial Kernel (RadialSVM), and LightGBM Random Forest (LightRF) were used for Regression.
  The top-performing model was RadialSVM with a test-set Rsq of 0.773, followed by GLMNET and LightRF with rsq of 0.772 and 0.735 respectively. 
  Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
  The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively. 
  Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Classification.
  The top-performing model was LinearSVM with a test-set Balanced Accuracy of 0.960, followed by LightRF and GLM with balanced_accuracy of 0.921 and 0.888 respectively. 
  Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
  The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively. 
  Generalized Linear Model (GLM), Support Vector Machine with Linear Kernel (LinearSVM), and LightGBM Random Forest (LightRF) were used for Regression.
  The top-performing model was LinearSVM with a test-set Rsq of 0.821, followed by GLM and LightRF with rsq of 0.817 and 0.661 respectively. 
  2026-10-08 02:51:42 <> Calibrating GLM resampled classification... [calibrate]
  
  <Resampled Classification Model>
  GLM (Generalized Linear Model)
  ⟳ Tested using 3 independent folds.
  ⟋ Calibrated using Isotonic Regression with 5 independent folds.
  
    <Resampled Classification Training Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.99 (0.02) => 0.95 (0.08)
          specificity: 0.99 (0.02) => 0.91 (0.09)
    balanced_accuracy: 0.99 (0.02) => 0.93 (0.07)
                  ppv: 0.99 (0.02) => 0.92 (0.08)
                  npv: 0.99 (0.02) => 0.95 (0.07)
                   f1: 0.99 (0.02) => 0.93 (0.07)
             accuracy: 0.99 (0.02) => 0.93 (0.07)
                  auc: 1.00 (3.7e-03) => 0.94 (0.05)
          brier_score: 0.01 (0.01) => 0.05 (0.05)
  
    <Resampled Classification Test Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.88 (0.13) => 0.94 (0.13)
          specificity: 0.90 (0.09) => 0.89 (0.20)
    balanced_accuracy: 0.89 (0.09) => 0.91 (0.12)
                  ppv: 0.90 (0.10) => 0.92 (0.15)
                  npv: 0.89 (0.12) => 0.94 (0.12)
                   f1: 0.89 (0.10) => 0.92 (0.11)
             accuracy: 0.89 (0.09) => 0.91 (0.12)
                  auc: 0.94 (0.06) => 0.92 (0.12)
          brier_score: 0.10 (0.09) => 0.08 (0.11)
  
  2026-10-08 02:51:43 </> Calibration done. [calibrate]
  2026-10-08 02:51:44 <> Calibrating CART resampled classification... [calibrate]
  
  <Resampled Classification Model>
  CART (Classification and Regression Trees)
  ⚙ Tuned using exhaustive grid search.
  ⟳ Tested using 3 independent folds.
  ⟋ Calibrated using Isotonic Regression with 5 independent folds.
  
    <Resampled Classification Training Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.94 (0.06) => 0.92 (0.08)
          specificity: 0.95 (0.06) => 0.92 (0.08)
    balanced_accuracy: 0.95 (0.01) => 0.92 (0.02)
                  ppv: 0.95 (0.06) => 0.93 (0.07)
                  npv: 0.95 (0.05) => 0.93 (0.07)
                   f1: 0.94 (0.01) => 0.92 (0.02)
             accuracy: 0.95 (0.01) => 0.92 (0.02)
                  auc: 0.95 (0.01) => 0.92 (0.02)
          brier_score: 0.05 (0.01) => 0.07 (0.02)
  
    <Resampled Classification Test Metrics (Pre => Post Calibration)>
    Showing mean (sd) across resamples, Pre => Post calibration.
          sensitivity: 0.92 (0.09) => 0.92 (0.14)
          specificity: 0.92 (0.09) => 0.92 (0.14)
    balanced_accuracy: 0.92 (0.01) => 0.92 (0.08)
                  ppv: 0.93 (0.08) => 0.94 (0.10)
                  npv: 0.93 (0.08) => 0.94 (0.11)
                   f1: 0.92 (0.02) => 0.92 (0.09)
             accuracy: 0.92 (0.01) => 0.92 (0.08)
                  auc: 0.91 (0.03) => 0.92 (0.08)
          brier_score: 0.07 (0.01) => 0.08 (0.06)
  
  2026-10-08 02:51:45 </> Calibration done. [calibrate]
  2026-10-08 02:51:45 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:51:45 ▶ [train]
  2026-10-08 02:51:45 Training set: 90 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:45     Test set: 10 cases x 5 features. [summarize_supervised]
  2026-10-08 02:51:45 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:51:45 Preprocessing... [train]
  2026-10-08 02:51:45 Training GLM Classification... [train]
  2026-10-08 02:51:45 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLM (Generalized Linear Model)
  ▣ Preprocessed using centering, scaling.
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         44           1
           versicolor          1          44
  
                       Overall  
          Sensitivity  0.978  
          Specificity  0.978  
    Balanced Accuracy  0.978  
                  Ppv  0.978  
                  Npv  0.978  
                   F1  0.978  
             Accuracy  0.978  
                  Auc  0.998  
          Brier Score  0.018  
  
       Positive Class virginica
  
    <Test Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica          5           0
           versicolor          1           4
  
                       Overall  
          Sensitivity  1.000  
          Specificity  0.800  
    Balanced Accuracy  0.900  
                  Ppv  0.833  
                  Npv  1.000  
                   F1  0.909  
             Accuracy  0.900  
                  Auc  1.000  
          Brier Score  0.093  
  
       Positive Class virginica
  
  2026-10-08 02:51:45 ✓ Done in 0.14 seconds. [train]
       rtemis Color System
  
       highlight_col: ░░▓▓██▓▓░░
            col_warn: ░░▓▓██▓▓░░
           col_error: ░░▓▓██▓▓░░
         col_success: ░░▓▓██▓▓░░
    col_preprocessor: ░░▓▓██▓▓░░
           col_decom: ░░▓▓██▓▓░░
           col_outer: ░░▓▓██▓▓░░
           col_tuner: ░░▓▓██▓▓░░
            col_info: ░░▓▓██▓▓░░
  Group counts:
  group
   Low   NS High 
     1   98    1 
  2026-10-08 02:52:01 ▶ [massGLM]
  2026-10-08 02:52:01 Scaling and centering 40 numeric features... [preprocess]
  2026-10-08 02:52:01 Preprocessing done. [preprocess]
  2026-10-08 02:52:01 Fitting 40 GLMs of family gaussian with 2 predictors each... [massGLM]
  2026-10-08 02:52:01 ✓ Done in 0.27 seconds. [massGLM]
  2026-10-08 02:52:01 Plotting coefficients for x1 x 40 outcomes. [`plot.rtemis::MassGLM`]
  Group counts:
  group
   Low   NS High 
     1   37    2 
  2026-10-08 02:52:01 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:52:01 ▶ [train]
  2026-10-08 02:52:01 Training set: 100 cases x 3 features. [summarize_supervised]
  2026-10-08 02:52:01 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:52:01 Training GLM Regression... [train]
  2026-10-08 02:52:01 Checking data is ready for training... ✔ [check_supervised]
  
  <Regression>
  GLM (Generalized Linear Model)
  
    <Training Regression Metrics>
     MAE: 0.84
     MSE: 1.04
    RMSE: 1.02
      R²: 0.69
  
  2026-10-08 02:52:01 ✓ Done in 0.11 seconds. [train]
  2026-10-08 02:52:01 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:52:01 ▶ [train]
  2026-10-08 02:52:01 Training set: 100 cases x 4 features. [summarize_supervised]
  2026-10-08 02:52:01 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: 1 } [get_n_workers]
  2026-10-08 02:52:01 Training GLM Classification... [train]
  2026-10-08 02:52:01 Checking data is ready for training... ✔ [check_supervised]
  
  <Classification>
  GLM (Generalized Linear Model)
  
    <Training Classification Metrics>
                       Predicted
            Reference  virginica  versicolor  
            virginica         49           1
           versicolor          1          49
  
                       Overall  
          Sensitivity  0.980  
          Specificity  0.980  
    Balanced Accuracy  0.980  
                  Ppv  0.980  
                  Npv  0.980  
                   F1  0.980  
             Accuracy  0.980  
                  Auc  0.997  
          Brier Score  0.019  
  
       Positive Class virginica
  
  2026-10-08 02:52:02 ✓ Done in 0.15 seconds. [train]
  2026-10-08 02:52:02 Checking data is ready for training... ✔ [check_supervised]
  2026-10-08 02:52:02 ▶ [train]
  2026-10-08 02:52:02 Training set: 100 cases x 3 features. [summarize_supervised]
  2026-10-08 02:52:02 // Max workers: c(`_R_CHECK_LIMIT_CORES_` = 1) { Algorithm: 1; Tuning: 1; Outer Resampling: c(`_R_CHECK_LIMIT_CORES_` = 1) } [get_n_workers]
  2026-10-08 02:52:02 <> Training GLM Regression using 3 independent folds... [train]
  2026-10-08 02:52:02 Input contains more than one column; stratifying on last. [resample]
  2026-10-08 02:52:02 </> Outer resampling done. [train]
  
  <Resampled Regression Model>
  GLM (Generalized Linear Model)
  ⟳ Tested using 3 independent folds.
  
    <Resampled Regression Training Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.821 (0.012)
     MSE: 1.008 (0.053)
    RMSE: 1.004 (0.026)
      R²: 0.697 (0.020)
  
    <Resampled Regression Test Metrics>
    Showing mean (sd) across resamples.
     MAE: 0.901 (0.029)
     MSE: 1.193 (0.074)
    RMSE: 1.092 (0.034)
      R²: 0.637 (0.047)
  
  2026-10-08 02:52:02 ✓ Done in 0.50 seconds. [train]
  [ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
  
  ══ Skipped tests (4) ═══════════════════════════════════════════════════════════
  • For local testing only; requires CSV file (3): 'test_ClusterConfig.R:19:3',
    'test_DecomposeConfig.R:19:3', 'test_SuperConfig.R:48:3'
  • empty test (1):
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test_Clustering.R:96:3'): cluster_DBSCAN() succeeds ─────────────────
  Error: approx must be a single, finite, nonnegative number.
  Backtrace:
      ▆
   1. └─rtemis::cluster(...) at test_Clustering.R:96:3
   2.   └─rtemis:::cluster_(config = config, x = x, verbosity = verbosity)
   3.     ├─S7::S7_dispatch()
   4.     └─rtemis (local) `method(cluster_, rtemis::DBSCANConfig)`(...)
   5.       └─dbscan::dbscan(...)
   6.         └─dbscan:::.validate_nonnegative_scalar(extra$approx %||% 0, "approx")
  
  [ FAIL 1 | WARN 4 | SKIP 4 | PASS 356 ]
  Error:
  ! Test failures.
  Execution halted
* checking PDF version of manual ... [24s] OK
* checking HTML version of manual ... [31s] OK
* DONE
Status: 1 ERROR