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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 Posit 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

1.3.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidymodels/dials

Homepage

https://dials.tidymodels.org

Maintainer

Last Published

July 30th, 2024

Functions in dials (1.3.0)

Near-zero variance parameters

dials: Tools for working with tuning parameters

Degrees of freedom (integer)

Neural network parameters

Parameters for exponents

Functions to finalize data-specific parameter ranges

grid_max_entropy

Max-entropy and latin hypercube grids

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

Parameters for possible engine parameters for earth models

Minkowski distance parameter

Possible engine parameters for lightbgm

Parameter for the effective minimum distance between embedded points

Word frequencies for removal

Initialization method for UMAP

Maximum number of retained tokens

Number of unique values for pre-processing

Create grids of tuning parameters

Number of randomly sampled predictors

harmonic_frequency

Harmonic Frequency

grid_space_filling

Space-filling parameter grids

Number of new features

Number of neighbors

Tools for creating new parameter objects

Number of cut-points for binning

Number of Clusters

Number of Computation Runs

Mixture of penalization terms

parameters_constr

Construct a new parameter set object

Amount of regularization/penalization

Gradient descent momentum parameter

Parameters for possible engine parameters for randomForest

MARS pruning methods

Tools for working with parameter ranges

Number of knots (integer)

Parameters for class-imbalance sampling

Information on tuning parameters within an object

Kernel Smoothness

Early stopping parameter

Proportion of Randomly Selected Predictors

regularization_factor

Parameters for possible engine parameters for ranger

Rolling summary statistic for moving windows

Parametric distributions for censored data

Proportion of predictors

Objects exported from other packages

prior_slab_dispersion

Bayesian PCA parameters

Kernel parameters

Text hashing parameters

Amount of supervision parameter

General thresholding parameter

Survival Model Link Function

Parameter to determine number of tokens in ngram

Placeholder for unknown parameter values

update.parameters

Update a single parameter in a parameter set

shrinkage_correlation

Parameters for possible engine parameters for sda models

select_features

Parameter to enable feature selection

validation_set_prop

Proportion of data used for validation

Succinct summary of parameter objects

Amount of Trimming

scheduler-param

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

regularization_method

Estimation methods for regularized models

Parameter for the moving window size

scale_pos_weight

Parameters for possible engine parameters for xgboost

Tools for working with parameter values

Kernel functions for distance weighting

Term frequency weighting methods

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

vocabulary_size

Number of tokens in vocabulary

Parameter for "double normalization" when creating token counts

adjust_deg_free

Parameters to adjust effective degrees of freedom

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

Laplace correction parameter

Parameter to determine which neighbors to use

Parameters for class weights for imbalanced problems

conditional_min_criterion

Parameters for possible engine parameters for partykit models

Support vector machine parameters

Parameters for possible engine parameters for Cubist

confidence_factor

Parameters for possible engine parameters for C5.0

Activation functions between network layers