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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.

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Version

Install

install.packages('dials')

Monthly Downloads

27,728

Version

0.0.9

License

GPL-2

Issues

Pull Requests

Stars

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Maintainer

Max Kuhn

Last Published

September 16th, 2020

Functions in dials (0.0.9)

cost

Support vector machine parameters
all_neighbors

Parameter to determine which neighbors to use
dials-package

dials: Tools for working with tuning parameters
Laplace

Laplace correction parameter
confidence_factor

Parameters for possible engine parameters for C5.0
degree

Parameters for exponents
extrapolation

Parameters for possible engine parameters for Cubist
activation

Activation functions between network layers
Chicago

Chicago Ridership Data
deg_free

Degrees of freedom (integer)
max_num_terms

Parameters for possible engine parameters for earth models
max_times

Word frequencies for removal
learn_rate

Learning rate
dist_power

Minkowski distance parameter
grid_regular

Create grids of tuning parameters
grid_max_entropy

Space-filling parameter grids
finalize

Functions to finalize data-specific parameter ranges
num_comp

Number of new features
freq_cut

Near-zero variance parameters
num_tokens

Parameter to determine number of tokens in ngram
new-param

Tools for creating new parameter objects
dropout

Neural network parameters
mtry

Number of randomly sampled predictors
encode_unit

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

Number of neighbors
predictor_prop

Proportion of predictors
num_breaks

Number of cut-points for binning
range_validate

Tools for working with parameter ranges
mixture

Mixture of penalization terms
min_unique

Number of unique values for pre-processing
parameters_constr

Construct a new parameter set object
over_ratio

Parameters for class-imbalance sampling
parameters

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
unknown

Placeholder for unknown parameter values
window_size

Parameter for the moving window size
prune_method

MARS pruning methods
penalty

Amount of regularization/penalization
smoothness

Kernel Smoothness
rbf_sigma

Kernel parameters
surv_dist

Parametric distributions for censored data
num_hash

Text hashing parameters
weight_func

Kernel functions for distance weighting
trees

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

Maximum number of retained tokens
type_sum.param

Succinct summary of parameter objects
pull_dials_object

Return a dials parameter object associated with parameters
threshold

General thresholding parameter
max_nodes

Parameters for possible engine parameters for randomForest
min_dist

Parameter for the effective minimum distance between embedded points
weight_scheme

Term frequency weighting methods
value_validate

Tools for working with parameter values
token

Token types
weight

Parameter for "double normalization" when creating token counts