Tools for Creating Tuning Parameter Values


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Documentation for package ‘dials’ version 0.0.3

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A B C D E F G H I L M N O P R S T U V W

-- A --

activation Activation functions between network layers

-- B --

batch_size Neural network parameters

-- C --

Chicago Chicago Ridership Data
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)
dist_power Minkowski distance parameter
dropout Neural network parameters

-- E --

epochs Neural network parameters

-- F --

finalize Functions to finalize data-specific parameter ranges
finalize.list Functions to finalize data-specific parameter ranges
finalize.param Functions to finalize data-specific parameter ranges
finalize.param_set Functions to finalize data-specific parameter ranges
freq_cut Near-zero variance parameters

-- 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.param_set 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.param_set 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.param_set 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.param_set Create grids of tuning parameters
grid_regular.workflow Create grids of tuning parameters

-- H --

has_unknowns Placeholder for unknown parameter values
hidden_units Neural network parameters

-- I --

is_unknown Placeholder for unknown parameter values

-- L --

Laplace Laplace correction parameter
learn_rate Learning rate
loss_reduction Parameter functions related to tree- and rule-based models.

-- M --

make_regular_grid Create grids of tuning parameters
margin Support vector machine parameters
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
mtry Number of randomly sampled predictors
mtry_long Number of randomly sampled 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
num_breaks Number of cut-points for binning
num_comp Number of new features
num_hash Text hashing parameters
num_terms Number of new features

-- O --

offset Kernel parameters
over_ratio Parameters for class-imbalance sampling

-- P --

param_set Information on tuning parameters within an object
param_set.default Information on tuning parameters within an object
param_set.list Information on tuning parameters within an object
param_set.param Information on tuning parameters within an object
penalty Amount of regularization/penalization
prod_degree Parameters for exponents
prune Parameter functions related to tree- and rule-based models.
prune_method MARS pruning methods

-- R --

range_get Tools for working with parameter ranges
range_set Tools for working with parameter ranges
range_validate Tools for working with parameter ranges
rbf_sigma Kernel parameters

-- 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
signed_hash Text hashing parameters
spline_degree Parameters for exponents
stations Chicago Ridership Data
surv_dist Parametric distributions for censored data

-- T --

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.

-- U --

under_ratio Parameters for class-imbalance sampling
unique_cut Near-zero variance parameters
unknown Placeholder for unknown parameter values
update.param_set Update a single parameter in a parameter set

-- V --

values_activation Activation functions between network layers
values_prune_method MARS pruning methods
values_surv_dist Parametric distributions for censored data
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

-- 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