dials v0.0.9


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Tools for Creating Tuning Parameter Values

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.



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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.


You can install the released version of dials from CRAN with:


You can install the development version from Github with:



This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Functions in dials

Name Description
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
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License GPL-2
URL https://dials.tidymodels.org, https://github.com/tidymodels/dials
BugReports https://github.com/tidymodels/dials/issues
Encoding UTF-8
LazyData true
ByteCompile true
RoxygenNote 7.1.1
VignetteBuilder knitr
NeedsCompilation no
Packaged 2020-09-16 01:13:17 UTC; max
Repository CRAN
Date/Publication 2020-09-16 05:40:02 UTC

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