Tools for Creating Tuning Parameter Values

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Documentation for package ‘dials’ version 1.4.3

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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
average_before_softmax Parameters for TabPFN models

-- B --

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

-- C --

cal_method_class Methods for model calibration
cal_method_reg Methods for model calibration
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_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.default 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.default 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.default 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_limit Limits for the range of predictions
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_estimators Parameters for TabPFN models
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 --

odds_link Ordinal Regression Link Functions (character)
ordinal_link Ordinal Regression Link Functions (character)
over_ratio Parameters for class-imbalance sampling

-- P --

parameters Create a parameter set
parameters.default Create a parameter set
parameters.list Create a parameter set
parameters.param Create a parameter set
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
prop_terms Proportion of top predictors
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_limits Limits for the range of predictions
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
rate_initial Parameters for neural network learning rate schedulers
rate_largest Parameters for neural network learning rate schedulers
rate_reduction Parameters for neural network learning rate schedulers
rate_schedule Parameters for neural network learning rate schedulers
rate_steps Parameters for neural network learning rate schedulers
rate_step_size Parameters for neural network learning rate schedulers
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
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
softmax_temperature Parameters for TabPFN models
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 --

tab-pfn-param Parameters for TabPFN models
target_weight Amount of supervision parameter
threshold General thresholding parameter
token Token types
training_set_limit Parameters for TabPFN models
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
upper_limit Limits for the range of predictions

-- V --

validation_set_prop Proportion of data used for validation
values_activation Activation functions between network layers
values_cal_cls Methods for model calibration
values_cal_reg Methods for model calibration
values_initial_umap Initialization method for UMAP
values_odds_link Ordinal Regression Link Functions (character)
values_ordinal_link Ordinal Regression Link Functions (character)
values_prune_method MARS pruning methods
values_regularization_method Estimation methods for regularized models
values_scheduler Parameters for neural network learning rate schedulers
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