A B C D E F G H I K L M N O P R S T U V W
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. |
batch_size | Neural network parameters |
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. |
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 |
epochs | Neural network parameters |
extrapolation | Parameters for possible engine parameters for Cubist |
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 |
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 |
harmonic_frequency | Harmonic Frequency |
has_unknowns | Placeholder for unknown parameter values |
hidden_units | Neural network parameters |
is_unknown | Placeholder for unknown parameter values |
kernel_offset | Kernel parameters |
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 |
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 |
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 |
over_ratio | Parameters for class-imbalance sampling |
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 |
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 |
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 |
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 |
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 |
validation_set_prop | Proportion of data used for validation |
values_activation | Activation functions between network layers |
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 |
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 |