R: Tools for Creating Tuning Parameter Values

# Tools for Creating Tuning Parameter Values

## Documentation for package ‘dials’ version 1.2.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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
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_latin_hypercube | Space-filling parameter grids
grid_latin_hypercube.list | Space-filling parameter grids
grid_latin_hypercube.param | Space-filling parameter grids
grid_latin_hypercube.parameters | Space-filling parameter grids
grid_latin_hypercube.workflow | Space-filling parameter grids
grid_max_entropy | Space-filling parameter grids
grid_max_entropy.list | Space-filling parameter grids
grid_max_entropy.param | Space-filling parameter grids
grid_max_entropy.parameters | Space-filling parameter grids
grid_max_entropy.workflow | Space-filling parameter grids
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_random.workflow | 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_regular.workflow | Create grids of tuning parameters

## -- H --

harmonic_frequency | Harmonic Frequency
has_unknowns | Placeholder for unknown parameter values
hidden_units | 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