All functions

Chicago

Chicago Ridership Data

Laplace()

Laplace correction parameter

activation() values_activation

Activation functions between network layers

all_neighbors()

Parameter to determine which neighbors to use

confidence_factor() no_global_pruning() predictor_winnowing() fuzzy_thresholding() rule_bands()

Parameters for possible engine parameters for C5.0

cost() svm_margin()

Support vector machine parameters

extrapolation() unbiased_rules() max_rules()

Parameters for possible engine parameters for Cubist

deg_free()

Degrees of freedom (integer)

degree() degree_int() spline_degree() prod_degree()

Parameters for exponents

dist_power()

Minkowski distance parameter

dropout() epochs() hidden_units() batch_size()

Neural network parameters

max_num_terms()

Parameters for possible engine parameters for earth models

finalize() get_p() get_log_p() get_n_frac() get_n_frac_range() get_n() get_rbf_range() get_batch_sizes()

Functions to finalize data-specific parameter ranges

freq_cut() unique_cut()

Near-zero variance parameters

grid_max_entropy() grid_latin_hypercube()

Space-filling parameter grids

grid_regular() make_regular_grid() grid_random()

Create grids of tuning parameters

learn_rate()

Learning rate

max_times() min_times()

Word frequencies for removal

max_tokens()

Maximum number of retained tokens

min_dist()

Parameter for the effective minimum distance between embedded points

min_unique()

Number of unique values for pre-processing

mixture()

Mixture of penalization terms

mtry() mtry_long()

Number of randomly sampled predictors

neighbors()

Number of neighbors

new_quant_param() new_qual_param()

Tools for creating new parameter objects

num_breaks()

Number of cut-points for binning

num_comp() num_terms()

Number of new features

num_tokens()

Parameter to determine number of tokens in ngram

over_ratio() under_ratio()

Parameters for class-imbalance sampling

parameters() param_set()

Information on tuning parameters within an object

penalty()

Amount of regularization/penalization

predictor_prop()

Proportion of predictors

prune_method() values_prune_method

MARS pruning methods

pull_dials_object()

Return a dials parameter object associated with parameters

max_nodes()

Parameters for possible engine parameters for randomForest

range_validate() range_get() range_set()

Tools for working with parameter ranges

regularization_factor() regularize_depth() significance_threshold() lower_quantile() splitting_rule() ranger_class_rules ranger_reg_rules ranger_split_rules num_random_splits()

Parameters for possible engine parameters for ranger

rbf_sigma() scale_factor() kernel_offset()

Kernel parameters

smoothness()

Kernel Smoothness

surv_dist() values_surv_dist

Parametric distributions for censored data

num_hash() signed_hash()

Text hashing parameters

threshold()

General thresholding parameter

token() values_token

Token types

trees() min_n() sample_size() sample_prop() loss_reduction() tree_depth() prune() cost_complexity()

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

unknown() is_unknown() has_unknowns()

Placeholder for unknown parameter values

update(<parameters>)

Update a single parameter in a parameter set

value_validate() value_seq() value_sample() value_transform() value_inverse() value_set()

Tools for working with parameter values

weight()

Parameter for "double normalization" when creating token counts

weight_func() values_weight_func

Kernel functions for distance weighting

weight_scheme() values_weight_scheme

Term frequency weighting methods

window_size()

Parameter for the moving window size