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dials

Overview

This package contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package.

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.
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Version

Install

install.packages('dials')

Monthly Downloads

27,728

Version

0.0.9

License

GPL-2

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidymodels/dials

Homepage

https://dials.tidymodels.org

Maintainer

Max Kuhn

Last Published

September 16th, 2020

Functions in dials (0.0.9)

Support vector machine parameters

Parameter to determine which neighbors to use

dials: Tools for working with tuning parameters

Laplace correction parameter

confidence_factor

Parameters for possible engine parameters for C5.0

Parameters for exponents

Parameters for possible engine parameters for Cubist

Activation functions between network layers

Chicago Ridership Data

Degrees of freedom (integer)

Parameters for possible engine parameters for earth models

Word frequencies for removal

Minkowski distance parameter

Create grids of tuning parameters

grid_max_entropy

Space-filling parameter grids

Functions to finalize data-specific parameter ranges

Number of new features

Near-zero variance parameters

Parameter to determine number of tokens in ngram

Tools for creating new parameter objects

Neural network parameters

Number of randomly sampled predictors

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

Number of neighbors

Proportion of predictors

Number of cut-points for binning

Tools for working with parameter ranges

Mixture of penalization terms

Number of unique values for pre-processing

parameters_constr

Construct a new parameter set object

Parameters for class-imbalance sampling

Information on tuning parameters within an object

regularization_factor

Parameters for possible engine parameters for ranger

update.parameters

Update a single parameter in a parameter set

Placeholder for unknown parameter values

Parameter for the moving window size

MARS pruning methods

Amount of regularization/penalization

Kernel Smoothness

Kernel parameters

Parametric distributions for censored data

Text hashing parameters

Kernel functions for distance weighting

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

Maximum number of retained tokens

Succinct summary of parameter objects

pull_dials_object

Return a dials parameter object associated with parameters

General thresholding parameter

Parameters for possible engine parameters for randomForest

Parameter for the effective minimum distance between embedded points

Term frequency weighting methods

Tools for working with parameter values

Parameter for "double normalization" when creating token counts