Function reference • dials

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

Reference

All functions