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dials

Overview

This package contains infrastructure to create and manage values of tuning parameters for the tidymodels packages. If you are looking for how to tune parameters in tidymodels, please look at the tune package and tidymodels.org.

The name reflects the idea that tuning predictive models can be like turning a set of dials on a complex machine under duress.

Installation

You can install the released version of dials from CRAN with:

install.packages("dials")

You can install the development version from Github with:

devtools::install_github("tidymodels/dials")

Contributing

Please note that the dials project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.

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Version

Install

install.packages('dials')

Monthly Downloads

24,893

Version

0.1.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidymodels/dials

Homepage

https://dials.tidymodels.org

Maintainer

Hannah Frick

Last Published

April 6th, 2022

Functions in dials (0.1.1)

Parameter to determine which neighbors to use

Support vector machine parameters

Parameters for class weights for imbalanced problems

confidence_factor

Parameters for possible engine parameters for C5.0

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

adjust_deg_free

Parameters to adjust effective degrees of freedom

conditional_min_criterion

Parameters for possible engine parameters for party models

Parameters for possible engine parameters for Cubist

Laplace correction parameter

Activation functions between network layers

Near-zero variance parameters

Number of cut-points for binning

Functions to finalize data-specific parameter ranges

Tools for creating new parameter objects

Create grids of tuning parameters

Class for converting parameter values back and forth to the unit range

Number of randomly sampled predictors

Minkowski distance parameter

dials: Tools for working with tuning parameters

Degrees of freedom (integer)

Parameters for exponents

Objects exported from other packages

General thresholding parameter

Kernel parameters

Text hashing parameters

Word frequencies for removal

Parameter to determine number of tokens in ngram

Parameters for class-imbalance sampling

Maximum number of retained tokens

Tools for working with parameter ranges

Number of neighbors

Proportion of predictors

Amount of regularization/penalization

regularization_method

Estimation methods for regularized models

Mixture of penalization terms

update.parameters

Update a single parameter in a parameter set

Parameter for the moving window size

Parameter functions related to tree- and rule-based models.

scale_pos_weight

Parameters for possible engine parameters for xgboost

select_features

Parameter to enable feature selection

regularization_factor

Parameters for possible engine parameters for ranger

Parameters for possible engine parameters for earth models

Neural network parameters

Number of new features

grid_max_entropy

Space-filling parameter grids

Information on tuning parameters within an object

Gradient descent momentum parameter

parameters_constr

Construct a new parameter set object

Number of knots (integer)

pull_dials_object

Return a dials parameter object associated with parameters

Parameter for the effective minimum distance between embedded points

Number of unique values for pre-processing

Early stopping parameter

Rolling summary statistic for moving windows

Tools for working with parameter values

Placeholder for unknown parameter values

Succinct summary of parameter objects

Parameter for "double normalization" when creating token counts

Survival Model Link Function

vocabulary_size

Number of tokens in vocabulary

Kernel Smoothness

shrinkage_correlation

Parameters for possible engine parameters for sda models

prior_slab_dispersion

Bayesian PCA parameters

Parametric distributions for censored data

MARS pruning methods

Parameters for possible engine parameters for randomForest

Kernel functions for distance weighting

Term frequency weighting methods