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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

This 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

30,765

Version

0.1.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidymodels/dials

Homepage

https://dials.tidymodels.org

Maintainer

Last Published

January 31st, 2022

Functions in dials (0.1.0)

adjust_deg_free

Parameters to adjust effective degrees of freedom

Activation functions between network layers

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

Support vector machine parameters

Parameter to determine which neighbors to use

Laplace correction parameter

confidence_factor

Parameters for possible engine parameters for C5.0

Parameters for class weights for imbalanced problems

conditional_min_criterion

Parameters for possible engine parameters for party models

Chicago Ridership Data

Number of cut-points for binning

Neural network parameters

Minkowski distance parameter

Number of unique values for pre-processing

Number of new features

Mixture of penalization terms

Parameters for possible engine parameters for earth models

Parameters for exponents

Maximum number of retained tokens

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

dials: Tools for working with tuning parameters

Near-zero variance parameters

Create grids of tuning parameters

Functions to finalize data-specific parameter ranges

Number of knots (integer)

grid_max_entropy

Space-filling parameter grids

Parameters for possible engine parameters for randomForest

Word frequencies for removal

Parameter for the effective minimum distance between embedded points

Parameters for class-imbalance sampling

Parameters for possible engine parameters for Cubist

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

Information on tuning parameters within an object

Succinct summary of parameter objects

Degrees of freedom (integer)

Number of neighbors

Tools for creating new parameter objects

Gradient descent momentum parameter

Tools for working with parameter ranges

Kernel Smoothness

regularization_factor

Parameters for possible engine parameters for ranger

Early stopping parameter

Tools for working with parameter values

vocabulary_size

Number of tokens in vocabulary

Proportion of predictors

prior_slab_dispersion

Bayesian PCA parameters

Survival Model Link Function

Parameter to determine number of tokens in ngram

Term frequency weighting methods

Parameter for the moving window size

Text hashing parameters

Number of randomly sampled predictors

Kernel parameters

parameters_constr

Construct a new parameter set object

select_features

Parameter to enable feature selection

Amount of regularization/penalization

shrinkage_correlation

Parameters for possible engine parameters for sda models

General thresholding parameter

pull_dials_object

Return a dials parameter object associated with parameters

Objects exported from other packages

MARS pruning methods

regularization_method

Estimation methods for regularized models

Rolling summary statistic for moving windows

Parametric distributions for censored data

update.parameters

Update a single parameter in a parameter set

Placeholder for unknown parameter values

Parameter for "double normalization" when creating token counts

Kernel functions for distance weighting

scale_pos_weight

Parameters for possible engine parameters for xgboost