Tidy Tuning Tools

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Documentation for package ‘tune’ version 1.3.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

.stash_last_result	Save most recent results to search path
.use_case_weights_with_yardstick	Determine if case weights should be passed on to yardstick
.use_case_weights_with_yardstick.hardhat_frequency_weights	Determine if case weights should be passed on to yardstick
.use_case_weights_with_yardstick.hardhat_importance_weights	Determine if case weights should be passed on to yardstick
ames_grid_search	Example Analysis of Ames Housing Data
ames_iter_search	Example Analysis of Ames Housing Data
ames_wflow	Example Analysis of Ames Housing Data
augment.last_fit	Augment data with holdout predictions
augment.resample_results	Augment data with holdout predictions
augment.tune_results	Augment data with holdout predictions
autoplot.tune_results	Plot tuning search results
collect_extracts	Obtain and format results produced by tuning functions
collect_extracts.tune_results	Obtain and format results produced by tuning functions
collect_metrics	Obtain and format results produced by tuning functions
collect_metrics.tune_results	Obtain and format results produced by tuning functions
collect_notes	Obtain and format results produced by tuning functions
collect_notes.tune_results	Obtain and format results produced by tuning functions
collect_predictions	Obtain and format results produced by tuning functions
collect_predictions.default	Obtain and format results produced by tuning functions
collect_predictions.tune_results	Obtain and format results produced by tuning functions
compute_metrics	Calculate and format metrics from tuning functions
compute_metrics.default	Calculate and format metrics from tuning functions
compute_metrics.tune_results	Calculate and format metrics from tuning functions
conf_bound	Acquisition function for scoring parameter combinations
conf_mat_resampled	Compute average confusion matrix across resamples
control_bayes	Control aspects of the Bayesian search process
control_last_fit	Control aspects of the last fit process
coord_obs_pred	Use same scale for plots of observed vs predicted values
example_ames_knn	Example Analysis of Ames Housing Data
expo_decay	Exponential decay function
exp_improve	Acquisition function for scoring parameter combinations
extract-tune	Extract elements of 'tune' objects
extract_fit_engine.tune_results	Extract elements of 'tune' objects
extract_fit_parsnip.tune_results	Extract elements of 'tune' objects
extract_model	Convenience functions to extract model
extract_mold.tune_results	Extract elements of 'tune' objects
extract_preprocessor.tune_results	Extract elements of 'tune' objects
extract_recipe.tune_results	Extract elements of 'tune' objects
extract_spec_parsnip.tune_results	Extract elements of 'tune' objects
extract_workflow.last_fit	Extract elements of 'tune' objects
extract_workflow.tune_results	Extract elements of 'tune' objects
filter_parameters	Remove some tuning parameter results
finalize_model	Splice final parameters into objects
finalize_recipe	Splice final parameters into objects
finalize_workflow	Splice final parameters into objects
fit_best	Fit a model to the numerically optimal configuration
fit_best.default	Fit a model to the numerically optimal configuration
fit_best.tune_results	Fit a model to the numerically optimal configuration
fit_resamples	Fit multiple models via resampling
fit_resamples.model_spec	Fit multiple models via resampling
fit_resamples.workflow	Fit multiple models via resampling
int_pctl.tune_results	Bootstrap confidence intervals for performance metrics
last_fit	Fit the final best model to the training set and evaluate the test set
last_fit.model_spec	Fit the final best model to the training set and evaluate the test set
last_fit.workflow	Fit the final best model to the training set and evaluate the test set
message_wrap	Write a message that respects the line width
parallelism	Support for parallel processing in tune
prob_improve	Acquisition function for scoring parameter combinations
select_best	Investigate best tuning parameters
select_best.default	Investigate best tuning parameters
select_best.tune_results	Investigate best tuning parameters
select_by_one_std_err	Investigate best tuning parameters
select_by_one_std_err.default	Investigate best tuning parameters
select_by_one_std_err.tune_results	Investigate best tuning parameters
select_by_pct_loss	Investigate best tuning parameters
select_by_pct_loss.default	Investigate best tuning parameters
select_by_pct_loss.tune_results	Investigate best tuning parameters
show_best	Investigate best tuning parameters
show_best.default	Investigate best tuning parameters
show_best.tune_results	Investigate best tuning parameters
show_notes	Display distinct errors from tune objects
tune_bayes	Bayesian optimization of model parameters.
tune_bayes.model_spec	Bayesian optimization of model parameters.
tune_bayes.workflow	Bayesian optimization of model parameters.
tune_grid	Model tuning via grid search
tune_grid.model_spec	Model tuning via grid search
tune_grid.workflow	Model tuning via grid search