Reporting with tidylearn

Plots

plot() picks the visualisation from the model type, and type narrows it further where a model supports more than one view.

Regression

model_reg <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")

# Actual vs predicted — one call
plot(model_reg, type = "actual_predicted")

Classification

split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)
model_clf <- tl_model(split$train, Species ~ ., method = "forest")

plot(model_clf, type = "confusion")

PCA

pca <- tidy_pca(USArrests, scale = TRUE)

tidy_pca_screeplot(pca)

tidy_pca_biplot(pca, label_obs = TRUE)

Regularisation

model_lasso <- tl_model(mtcars, mpg ~ ., method = "lasso")

tl_plot_regularization_path(model_lasso)

tl_plot_regularization_cv(model_lasso)

Tables

tl_table() mirrors the plot interface, dispatching on model type and an optional type:

tl_table(model)                       # auto-selects the best table type
tl_table(model, type = "coefficients") # specific type

Evaluation Metrics

tl_table_metrics(model_reg)

Metric	Value
Model Evaluation Metrics
Rmse	2.4689
Mae	1.9015
Rsq	0.8268
tidylearn \| linear (regression) \| mpg ~ wt + hp \| n = 32

Coefficients

For linear and logistic models, the table includes standard errors, test statistics, and p-values, with significant terms highlighted:

tl_table_coefficients(model_reg)

Term	Estimate	Std. Error	t value	p
Linear Model Coefficients
(Intercept)	37.2273	1.5988	23.2847	2.57 × 10⁻²⁰	*
wt	−3.8778	0.6327	−6.1287	1.12 × 10⁻⁶	*
hp	−0.0318	0.0090	−3.5187	1.45 × 10⁻³	*
tidylearn \| linear (regression) \| mpg ~ wt + hp \| n = 32

conf_int = TRUE adds a confidence interval, and level sets its width:

tl_table_coefficients(model_reg, conf_int = TRUE, level = 0.9)

Term	Estimate	Std. Error	Lower 90%	Upper 90%	t value	p
Linear Model Coefficients
Wald 90% intervals
(Intercept)	37.2273	1.5988	34.5107	39.9438	23.2847	2.57 × 10⁻²⁰	*
wt	−3.8778	0.6327	−4.9529	−2.8027	−6.1287	1.12 × 10⁻⁶	*
hp	−0.0318	0.0090	−0.0471	−0.0164	−3.5187	1.45 × 10⁻³	*
tidylearn \| linear (regression) \| mpg ~ wt + hp \| n = 32

These are Wald intervals, built from the standard errors in the column beside them, so the interval and the p-value in a row always agree about whether zero is excluded. For a logistic model, exponentiate = TRUE reports odds ratios instead of log odds.

For regularised models, coefficients are sorted by magnitude and zero coefficients are greyed out. There is no interval to add — glmnet reports no standard errors, and conf_int = TRUE is an error here rather than a column of NA:

tl_table_coefficients(model_lasso)

Term	Coefficient	\|Coefficient\|
Lasso Coefficients
lambda = 1.399 (1se)
(Intercept)	33.9405	33.9405
wt	−2.3659	2.3659
cyl	−0.8430	0.8430
hp	−0.0070	0.0070
disp	0.0000	0.0000
drat	0.0000	0.0000
qsec	0.0000	0.0000
vs	0.0000	0.0000
am	0.0000	0.0000
gear	0.0000	0.0000
carb	0.0000	0.0000
tidylearn \| lasso (regression) \| mpg ~ . \| n = 32

The numbers behind these tables come from tl_coefficients(), which takes the same arguments, returns a tibble, and does not need gt installed:

tl_coefficients(model_reg, conf_int = TRUE)
#> # A tibble: 3 × 7
#>   term        estimate std_error conf_low conf_high statistic  p_value
#>   <chr>          <dbl>     <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)  37.2      1.60     34.0      40.5        23.3  2.57e-20
#> 2 wt           -3.88     0.633    -5.17     -2.58       -6.13 1.12e- 6
#> 3 hp           -0.0318   0.00903  -0.0502   -0.0133     -3.52 1.45e- 3

Confusion Matrix

A formatted confusion matrix with correct predictions highlighted on the diagonal:

tl_table_confusion(model_clf, new_data = split$test)

Actual	Predicted
Confusion Matrix
Actual	setosa	versicolor	virginica
setosa	15	0	0
versicolor	0	15	0
virginica	0	2	13
tidylearn \| forest (classification) \| Species ~ . \| n = 45

Feature Importance

A ranked importance table with a colour gradient:

tl_table_importance(model_clf)

Feature	Importance
Feature Importance
Top 4 features
Petal.Length	100.00
Petal.Width	90.41
Sepal.Length	29.86
Sepal.Width	13.11
tidylearn \| forest (classification) \| Species ~ . \| n = 105

PCA Variance Explained

Cumulative variance is coloured green to highlight how many components are needed:

pca_model <- tl_model(USArrests, method = "pca")
tl_table_variance(pca_model)

Component	Std. Dev.	Variance	Proportion	Cumulative
PCA Variance Explained
PC1	1.5749	2.4802	62.0%	62.0%
PC2	0.9949	0.9898	24.7%	86.8%
PC3	0.5971	0.3566	8.9%	95.7%
PC4	0.4164	0.1734	4.3%	100.0%
tidylearn \| pca \| n = 50

PCA Loadings

A diverging red–blue colour scale highlights strong positive and negative loadings:

tl_table_loadings(pca_model)

Variable	PC1	PC2	PC3	PC4
PCA Loadings
Murder	−0.536	−0.418	0.341	0.649
Assault	−0.583	−0.188	0.268	−0.743
UrbanPop	−0.278	0.873	0.378	0.134
Rape	−0.543	0.167	−0.818	0.089
tidylearn \| pca \| n = 50

Cluster Summary

Cluster sizes and mean feature values:

km <- tl_model(iris[, 1:4], method = "kmeans", k = 3)
tl_table_clusters(km)

Cluster	Size	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
Cluster Summary
kmeans \| 3 clusters
1	38	6.85	3.07	5.74	2.07
2	50	5.01	3.43	1.46	0.25
3	62	5.90	2.75	4.39	1.43
tidylearn \| kmeans \| n = 150

Model Comparison

Compare multiple models side-by-side:

m1 <- tl_model(split$train, Species ~ ., method = "svm")
m2 <- tl_model(split$train, Species ~ ., method = "forest")
m3 <- tl_model(split$train, Species ~ ., method = "tree")

tl_table_comparison(
  m1, m2, m3,
  new_data = split$test,
  names = c("SVM", "Random Forest", "Decision Tree")
)

Metric	SVM	Random Forest	Decision Tree
Model Comparison
3 models compared
Accuracy	0.9111	0.9556	0.8889
tidylearn \| n = 45

Interactive Reporting with plotly

Most plot functions return a ggplot2 object, and ggplotly() takes any of those without special handling:

library(plotly)

ggplotly(plot(model_reg, type = "actual_predicted"))
ggplotly(tidy_pca_biplot(pca, label_obs = TRUE))
ggplotly(tl_plot_regularization_path(model_lasso))

These do not return a single ggplot2 object:

plot_dendrogram(), which plot() uses for an hclust model, tl_plot_tree() and tl_plot_nn_architecture() draw with base graphics.
tl_plot_xgboost_tree() returns a DiagrammeR widget, tl_plot_deep_architecture() draws the keras model diagram, and visualize_rules(method = "paracoord") draws with grid.
tl_diagnostic_dashboard() and plot_cluster_comparison() return an arranged grid of panels. plot(model, type = "diagnostics") and create_cluster_dashboard() return a list of ggplot2 objects, which ggplotly() takes one at a time.
plot() on a tl_explore() result draws its plot and returns the exploration result, and tl_dashboard() returns a Shiny app.

Putting It Together

Fit, score, look, drill in — the four calls that make up most reporting sections:

# Fit
model <- tl_model(split$train, Species ~ ., method = "forest")

# Evaluate
tl_table_metrics(model, new_data = split$test)

Metric	Value
Model Evaluation Metrics
Accuracy	0.9333
tidylearn \| forest (classification) \| Species ~ . \| n = 45


# Visualise
plot(model, type = "confusion")


# Drill into feature importance
tl_table_importance(model, top_n = 4)

Feature	Importance
Feature Importance
Top 4 features
Petal.Length	100.00
Petal.Width	88.90
Sepal.Length	27.46
Sepal.Width	8.69
tidylearn \| forest (classification) \| Species ~ . \| n = 105

Swap method = "forest" for method = "tree" and the reporting code above works without modification. tl_table_importance() covers the tree-based and regularised methods only, so for a method such as "svm" or "nn", leave out the last call.