Laplace Laplace correction parameter 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 bart-param 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 Parameters for possible engine parameters for partykit models confidence_factor Parameters for possible engine parameters for C5.0 cost Support vector machine parameters deg_free Degrees of freedom (integer) degree Parameters for exponents dist_power Minkowski distance parameter dropout Neural network parameters extrapolation Parameters for possible engine parameters for Cubist 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 harmonic_frequency Harmonic Frequency initial_umap Initialization method for UMAP 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 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 Number of randomly sampled predictors mtry_prop Proportion of Randomly Selected Predictors neighbors Number of neighbors new-param Tools for creating new parameter objects 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_runs Number of Computation Runs num_tokens Parameter to determine number of tokens in ngram over_ratio Parameters for class-imbalance sampling parameters Information on tuning parameters within an object penalty Amount of regularization/penalization predictor_prop Proportion of predictors prior_slab_dispersion Bayesian PCA parameters prune_method MARS pruning methods range_validate Tools for working with parameter ranges rbf_sigma Kernel parameters regularization_factor Parameters for possible engine parameters for ranger regularization_method Estimation methods for regularized models 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 smoothness Kernel Smoothness stop_iter Early stopping parameter summary_stat Rolling summary statistic for moving windows surv_dist Parametric distributions for censored data survival_link Survival Model Link Function target_weight Amount of supervision parameter threshold General thresholding parameter token Token types trees Parameter functions related to tree- and rule-based models. trim_amount Amount of Trimming unknown Placeholder for unknown parameter values update.parameters Update a single parameter in a parameter set validation_set_prop Proportion of data used for validation value_validate Tools for working with parameter values vocabulary_size Number of tokens in vocabulary 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