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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:

# install.packages("pak")
pak::pak("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

30,745

Version

1.2.0

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 3rd, 2023

Functions in dials (1.2.0)

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

Functions to finalize data-specific parameter ranges

dials: Tools for working with tuning parameters

Parameters for class weights for imbalanced problems

Degrees of freedom (integer)

conditional_min_criterion

Parameters for possible engine parameters for partykit models

Parameters for exponents

Possible engine parameters for lightbgm

Parameter for the effective minimum distance between embedded points

Minkowski distance parameter

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

Mixture of penalization terms

Parameters for possible engine parameters for earth models

Neural network parameters

confidence_factor

Parameters for possible engine parameters for C5.0

Gradient descent momentum parameter

Number of neighbors

Number of new features

MARS pruning methods

Tools for creating new parameter objects

Number of randomly sampled predictors

Number of knots (integer)

Proportion of Randomly Selected Predictors

parameters_constr

Construct a new parameter set object

Amount of regularization/penalization

pull_dials_object

Return a dials parameter object associated with parameters

Near-zero variance parameters

Number of unique values for pre-processing

Parameters for possible engine parameters for randomForest

grid_max_entropy

Space-filling parameter grids

Objects exported from other packages

Number of Computation Runs

regularization_method

Estimation methods for regularized models

Amount of Trimming

Parameter to determine number of tokens in ngram

Create grids of tuning parameters

vocabulary_size

Number of tokens in vocabulary

select_features

Parameter to enable feature selection

Proportion of predictors

scheduler-param

Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.

Word frequencies for removal

Tools for working with parameter ranges

regularization_factor

Parameters for possible engine parameters for ranger

Maximum number of retained tokens

Parameter for "double normalization" when creating token counts

Succinct summary of parameter objects

prior_slab_dispersion

Bayesian PCA parameters

Kernel parameters

Kernel functions for distance weighting

General thresholding parameter

Text hashing parameters

Term frequency weighting methods

Parameters for class-imbalance sampling

Information on tuning parameters within an object

Early stopping parameter

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

harmonic_frequency

Harmonic Frequency

Number of cut-points for binning

Kernel Smoothness

shrinkage_correlation

Parameters for possible engine parameters for sda models

Number of Clusters

Rolling summary statistic for moving windows

validation_set_prop

Proportion of data used for validation

Tools for working with parameter values

Survival Model Link Function

Parameter for the moving window size

Parametric distributions for censored data

scale_pos_weight

Parameters for possible engine parameters for xgboost

Placeholder for unknown parameter values

update.parameters

Update a single parameter in a parameter set

Parameter to determine which neighbors to use

Laplace correction parameter

adjust_deg_free

Parameters to adjust effective degrees of freedom

Support vector machine parameters

Parameters for possible engine parameters for Cubist

Activation functions between network layers