Tools for Creating Tuning Parameter Values

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Documentation for package ‘dials’ version 1.3.0

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A B C D E F G H I K L M N O P R S T U V W

-- A --

activation Activation functions between network layers
activation_2 Activation functions between network layers
adjust_deg_free Parameters to adjust effective degrees of freedom
all_neighbors Parameter to determine which neighbors to use

-- B --

bart-param Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
batch_size Neural network parameters

-- C --

class_weights Parameters for class weights for imbalanced problems
conditional_min_criterion Parameters for possible engine parameters for partykit models
conditional_test_statistic Parameters for possible engine parameters for partykit models
conditional_test_type Parameters for possible engine parameters for partykit models
confidence_factor Parameters for possible engine parameters for C5.0
cost Support vector machine parameters
cost_complexity Parameter functions related to tree- and rule-based models.

-- D --

degree Parameters for exponents
degree_int Parameters for exponents
deg_free Degrees of freedom (integer)
diagonal_covariance Parameters for possible engine parameters for sda models
dist_power Minkowski distance parameter
dropout Neural network parameters

-- E --

epochs Neural network parameters
extrapolation Parameters for possible engine parameters for Cubist

-- F --

finalize Functions to finalize data-specific parameter ranges
finalize.default Functions to finalize data-specific parameter ranges
finalize.list Functions to finalize data-specific parameter ranges
finalize.logical Functions to finalize data-specific parameter ranges
finalize.param Functions to finalize data-specific parameter ranges
finalize.parameters Functions to finalize data-specific parameter ranges
freq_cut Near-zero variance parameters
fuzzy_thresholding Parameters for possible engine parameters for C5.0

-- G --

get_batch_sizes Functions to finalize data-specific parameter ranges
get_log_p Functions to finalize data-specific parameter ranges
get_n Functions to finalize data-specific parameter ranges
get_n_frac Functions to finalize data-specific parameter ranges
get_n_frac_range Functions to finalize data-specific parameter ranges
get_p Functions to finalize data-specific parameter ranges
get_rbf_range Functions to finalize data-specific parameter ranges
grid_random Create grids of tuning parameters
grid_random.list Create grids of tuning parameters
grid_random.param Create grids of tuning parameters
grid_random.parameters Create grids of tuning parameters
grid_regular Create grids of tuning parameters
grid_regular.list Create grids of tuning parameters
grid_regular.param Create grids of tuning parameters
grid_regular.parameters Create grids of tuning parameters
grid_space_filling Space-filling parameter grids
grid_space_filling.list Space-filling parameter grids
grid_space_filling.param Space-filling parameter grids
grid_space_filling.parameters Space-filling parameter grids

-- H --

harmonic_frequency Harmonic Frequency
has_unknowns Placeholder for unknown parameter values
hidden_units Neural network parameters
hidden_units_2 Neural network parameters

-- I --

initial_umap Initialization method for UMAP
is_unknown Placeholder for unknown parameter values

-- K --

kernel_offset Kernel parameters

-- L --

Laplace Laplace correction parameter
learn_rate Learning rate
loss_reduction Parameter functions related to tree- and rule-based models.
lower_quantile Parameters for possible engine parameters for ranger

-- M --

max_nodes Parameters for possible engine parameters for randomForest
max_num_terms Parameters for possible engine parameters for earth models
max_rules Parameters for possible engine parameters for Cubist
max_times Word frequencies for removal
max_tokens Maximum number of retained tokens
min_dist Parameter for the effective minimum distance between embedded points
min_n Parameter functions related to tree- and rule-based models.
min_times Word frequencies for removal
min_unique Number of unique values for pre-processing
mixture Mixture of penalization terms
momentum Gradient descent momentum parameter
mtry Number of randomly sampled predictors
mtry_long Number of randomly sampled predictors
mtry_prop Proportion of Randomly Selected Predictors

-- N --

neighbors Number of neighbors
new-param Tools for creating new parameter objects
new_qual_param Tools for creating new parameter objects
new_quant_param Tools for creating new parameter objects
no_global_pruning Parameters for possible engine parameters for C5.0
num_breaks Number of cut-points for binning
num_clusters Number of Clusters
num_comp Number of new features
num_hash Text hashing parameters
num_knots Number of knots (integer)
num_leaves Possible engine parameters for lightbgm
num_random_splits Parameters for possible engine parameters for ranger
num_runs Number of Computation Runs
num_terms Number of new features
num_tokens Parameter to determine number of tokens in ngram

-- O --

over_ratio Parameters for class-imbalance sampling

-- P --

parameters Information on tuning parameters within an object
parameters.default Information on tuning parameters within an object
parameters.list Information on tuning parameters within an object
parameters.param Information on tuning parameters within an object
penalty Amount of regularization/penalization
penalty_L1 Parameters for possible engine parameters for xgboost
penalty_L2 Parameters for possible engine parameters for xgboost
predictor_prop Proportion of predictors
predictor_winnowing Parameters for possible engine parameters for C5.0
prior_mixture_threshold Bayesian PCA parameters
prior_outcome_range Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
prior_slab_dispersion Bayesian PCA parameters
prior_terminal_node_coef Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
prior_terminal_node_expo Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
prod_degree Parameters for exponents
prune Parameter functions related to tree- and rule-based models.
prune_method MARS pruning methods

-- R --

ranger_class_rules Parameters for possible engine parameters for ranger
ranger_reg_rules Parameters for possible engine parameters for ranger
ranger_split_rules Parameters for possible engine parameters for ranger
range_get Tools for working with parameter ranges
range_set Tools for working with parameter ranges
range_validate Tools for working with parameter ranges
rate_decay Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_initial Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_largest Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_reduction Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_schedule Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_steps Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rate_step_size Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
rbf_sigma Kernel parameters
regularization_factor Parameters for possible engine parameters for ranger
regularization_method Estimation methods for regularized models
regularize_depth Parameters for possible engine parameters for ranger
rule_bands Parameters for possible engine parameters for C5.0

-- S --

sample_prop Parameter functions related to tree- and rule-based models.
sample_size Parameter functions related to tree- and rule-based models.
scale_factor Kernel parameters
scale_pos_weight Parameters for possible engine parameters for xgboost
scheduler-param Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
select_features Parameter to enable feature selection
shrinkage_correlation Parameters for possible engine parameters for sda models
shrinkage_frequencies Parameters for possible engine parameters for sda models
shrinkage_variance Parameters for possible engine parameters for sda models
signed_hash Text hashing parameters
significance_threshold Parameters for possible engine parameters for ranger
smoothness Kernel Smoothness
spline_degree Parameters for exponents
splitting_rule Parameters for possible engine parameters for ranger
stop_iter Early stopping parameter
summary_stat Rolling summary statistic for moving windows
survival_link Survival Model Link Function
surv_dist Parametric distributions for censored data
svm_margin Support vector machine parameters

-- T --

target_weight Amount of supervision parameter
threshold General thresholding parameter
token Token types
trees Parameter functions related to tree- and rule-based models.
tree_depth Parameter functions related to tree- and rule-based models.
trim_amount Amount of Trimming

-- U --

unbiased_rules Parameters for possible engine parameters for Cubist
under_ratio Parameters for class-imbalance sampling
unique_cut Near-zero variance parameters
unknown Placeholder for unknown parameter values
update.parameters Update a single parameter in a parameter set

-- V --

validation_set_prop Proportion of data used for validation
values_activation Activation functions between network layers
values_initial_umap Initialization method for UMAP
values_prune_method MARS pruning methods
values_regularization_method Estimation methods for regularized models
values_scheduler Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
values_summary_stat Rolling summary statistic for moving windows
values_survival_link Survival Model Link Function
values_surv_dist Parametric distributions for censored data
values_test_statistic Parameters for possible engine parameters for partykit models
values_test_type Parameters for possible engine parameters for partykit models
values_token Token types
values_weight_func Kernel functions for distance weighting
values_weight_scheme Term frequency weighting methods
value_inverse Tools for working with parameter values
value_sample Tools for working with parameter values
value_seq Tools for working with parameter values
value_set Tools for working with parameter values
value_transform Tools for working with parameter values
value_validate Tools for working with parameter values
vocabulary_size Number of tokens in vocabulary

-- W --

weight Parameter for '"double normalization"' when creating token counts
weight_func Kernel functions for distance weighting
weight_scheme Term frequency weighting methods
window_size Parameter for the moving window size