Chicago                 Chicago Ridership Data
Laplace                 Laplace correction parameter
activation              Activation functions between network layers
cost                    Support vector machine parameters
deg_free                Degrees of freedom (integer)
degree                  Parameters for exponents
dist_power              Minkowski distance parameter
dropout                 Neural network parameters
finalize                Functions to finalize data-specific parameter
                        ranges
freq_cut                Near-zero variance parameters
grid_max_entropy        Space-filling parameter grids
grid_regular            Create grids of tuning parameters
learn_rate              Learning rate
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_unique              Number of unique values for pre-processing
mixture                 Mixture of penalization terms
mtry                    Number of randomly sampled predictors
neighbors               Number of neighbors
new-param               Tools for creating new parameter objects
num_breaks              Number of cut-points for binning
num_comp                Number of new features
num_hash                Text hashing parameters
over_ratio              Parameters for class-imbalance sampling
parameters              Information on tuning parameters within an
                        object
penalty                 Amount of regularization/penalization
prune_method            MARS pruning methods
range_validate          Tools for working with parameter ranges
rbf_sigma               Kernel parameters
smoothness              Kernel Smoothness
surv_dist               Parametric distributions for censored data
threshold               General thresholding parameter
token                   Token types
trees                   Parameter functions related to tree- and
                        rule-based models.
unknown                 Placeholder for unknown parameter values
update.parameters       Update a single parameter in a parameter set
value_validate          Tools for working with parameter values
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
