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Fit many models

tune_grid()
Model tuning via grid search
tune_bayes()
Bayesian optimization of model parameters.
expo_decay()
Exponential decay function
prob_improve() exp_improve() conf_bound()
Acquisition function for scoring parameter combinations
melodie_grid()
Model tuning via grid search
fit_resamples()
Fit multiple models via resampling
control_grid() control_resamples() new_backend_options()
Control aspects of the grid search process
control_bayes()
Control aspects of the Bayesian search process
parallelism
Support for parallel processing in tune

Fit one model

fit_best()
Fit a model to the numerically optimal configuration
last_fit()
Fit the final best model to the training set and evaluate the test set
finalize_model() finalize_recipe() finalize_workflow() finalize_tailor()
Splice final parameters into objects
control_last_fit()
Control aspects of the last fit process

Inspect results

collect_predictions() collect_metrics() collect_notes() collect_extracts()
Obtain and format results produced by tuning functions
show_notes()
Display distinct errors from tune objects
show_best() select_best() select_by_pct_loss() select_by_one_std_err()
Investigate best tuning parameters
filter_parameters()
Remove some tuning parameter results
autoplot(<tune_results>)
Plot tuning search results
coord_obs_pred()
Use same scale for plots of observed vs predicted values
conf_mat_resampled()
Compute average confusion matrix across resamples

Miscellaneous

Developer functions

merge(<recipe>) merge(<model_spec>)
Merge parameter grid values into objects
parameters(<workflow>) parameters(<model_spec>) parameters(<recipe>) deprecated
Determination of parameter sets for other objects
message_wrap()
Write a message that respects the line width
.use_case_weights_with_yardstick()
Determine if case weights should be passed on to yardstick
.stash_last_result()
Save most recent results to search path