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Parameter Sets

parameters()
Information on tuning parameters within an object
update(<parameters>)
Update a single parameter in a parameter set
pull_dials_object()
Return a dials parameter object associated with parameters
range_validate() range_get() range_set()
Tools for working with parameter ranges
value_validate() value_seq() value_sample() value_transform() value_inverse() value_set()
Tools for working with parameter values

Grid Creation

grid_max_entropy() grid_latin_hypercube()
Space-filling parameter grids
grid_regular() grid_random()
Create grids of tuning parameters

Parameter Objects

activation() values_activation
Activation functions between network layers
adjust_deg_free()
Parameters to adjust effective degrees of freedom
all_neighbors()
Parameter to determine which neighbors to use
prior_terminal_node_coef() prior_terminal_node_expo() prior_outcome_range()
Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
class_weights()
Parameters for class weights for imbalanced problems
conditional_min_criterion() values_test_type conditional_test_type() values_test_statistic conditional_test_statistic()
Parameters for possible engine parameters for partykit models
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
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
extrapolation() unbiased_rules() max_rules()
Parameters for possible engine parameters for Cubist
freq_cut() unique_cut()
Near-zero variance parameters
Laplace()
Laplace correction parameter
learn_rate()
Learning rate
max_nodes()
Parameters for possible engine parameters for randomForest
max_num_terms()
Parameters for possible engine parameters for earth models
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
momentum()
Gradient descent momentum parameter
mtry() mtry_long()
Number of randomly sampled predictors
mtry_prop()
Proportion of Randomly Selected Predictors
neighbors()
Number of neighbors
num_breaks()
Number of cut-points for binning
num_comp() num_terms()
Number of new features
num_hash() signed_hash()
Text hashing parameters
num_knots()
Number of knots (integer)
num_tokens()
Parameter to determine number of tokens in ngram
over_ratio() under_ratio()
Parameters for class-imbalance sampling
penalty()
Amount of regularization/penalization
predictor_prop()
Proportion of predictors
prior_slab_dispersion() prior_mixture_threshold()
Bayesian PCA parameters
prune_method() values_prune_method
MARS pruning methods
rbf_sigma() scale_factor() kernel_offset()
Kernel parameters
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
regularization_method() values_regularization_method
Estimation methods for regularized models
scale_pos_weight() penalty_L2() penalty_L1()
Parameters for possible engine parameters for xgboost
select_features()
Parameter to enable feature selection
shrinkage_correlation() shrinkage_variance() shrinkage_frequencies() diagonal_covariance()
Parameters for possible engine parameters for sda models
smoothness()
Kernel Smoothness
stop_iter()
Early stopping parameter
summary_stat() values_summary_stat
Rolling summary statistic for moving windows
surv_dist() values_surv_dist
Parametric distributions for censored data
survival_link() values_survival_link
Survival Model Link Function
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.
vocabulary_size()
Number of tokens in vocabulary
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

Finalizing Parameters

Developer Tools

encode_unit()
Class for converting parameter values back and forth to the unit range
new_quant_param() new_qual_param()
Tools for creating new parameter objects
parameters_constr()
Construct a new parameter set object
unknown() is_unknown() has_unknowns()
Placeholder for unknown parameter values