.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 |
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 |