tune v0.1.2

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Tidy Tuning Tools

The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.

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tune

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Overview

The goal of tune is to facilitate hyperparameter tuning for the tidymodels packages. It relies heavily on recipes, parsnip, and dials.

Installation

Install from CRAN:

install.packages("tune", repos = "http://cran.r-project.org") #or your local mirror

or you can install the current development version using:

devtools::install_github("tidymodels/tune")

Examples

There are several package vignettes, as well as articles available at tidymodels.org, demonstrating how to use tune.

Good places to begin include:

More advanced resources available are:

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Functions in tune

Name Description
control_bayes Control aspects of the Bayesian search process
autoplot.tune_results Plot tuning search results
coord_obs_pred Use same scale for plots of observed vs predicted values
augment.tune_results Augment data with holdout predictions
collect_predictions Obtain and format results produced by tuning functions
example_ames_knn Example Analysis of Ames Housing Data
check_rset Get colors for tune text.
expo_decay Exponential decay function
finalize_model Splice final parameters into objects
extract_recipe Convenience functions to extract model or recipe
filter_parameters Remove some tuning parameter results
min_grid.model_spec Determine the minimum set of model fits
outcome_names Determine names of the outcome data in a workflow
tune A placeholder function for argument values that are to be tuned.
fit_resamples Fit multiple models via resampling
.get_tune_parameters Various accessor functions
conf_mat_resampled Compute average confusion matrix across resamples
last_fit Fit the final best model to the training set and evaluate the test set
control_grid Control aspects of the grid search process
merge.recipe Merge parameter grid values into objects
message_wrap Write a message that respects the line width
reexports Objects exported from other packages
required_pkgs.model_spec Determine packages required by objects
load_pkgs Quietly load package namespace
tune_grid Model tuning via grid search
show_best Investigate best tuning parameters
tunable.model_spec Find recommended methods for generating parameter values
tune_bayes Bayesian optimization of model parameters.
tune_args Determine arguments tagged for tuning
parameters.workflow Determination of parameter sets for other objects
prob_improve Acquisition function for scoring parameter combinations
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