# Tidy Tuning Tools

## Documentation for package ‘tune’ version 2.0.1

- DESCRIPTION file
- Package NEWS
- Package README

## 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_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_tailor | 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