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Create a forest model config object

Source: R/config.R
createForestModelConfig.Rd

Create a forest model config object

Usage

createForestModelConfig(
  feature_types = NULL,
  num_trees = NULL,
  num_features = NULL,
  num_observations = NULL,
  variable_weights = NULL,
  leaf_dimension = 1,
  alpha = 0.95,
  beta = 2,
  min_samples_leaf = 5,
  max_depth = -1,
  leaf_model_type = 1,
  leaf_model_scale = NULL,
  variance_forest_shape = 1,
  variance_forest_scale = 1,
  cutpoint_grid_size = 100
)

Arguments

feature_types

Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)

num_trees

Number of trees in the forest being sampled

num_features

Number of features in training dataset

num_observations

Number of observations in training dataset

variable_weights

Vector specifying sampling probability for all p covariates in ForestDataset

leaf_dimension

Dimension of the leaf model (default: 1)

alpha

Root node split probability in tree prior (default: 0.95)

beta

Depth prior penalty in tree prior (default: 2.0)

min_samples_leaf

Minimum number of samples in a tree leaf (default: 5)

max_depth

Maximum depth of any tree in the ensemble in the model. Setting to -1 does not enforce any depth limits on trees. Default: -1.

leaf_model_type

Integer specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression). Default: 0.

leaf_model_scale

Scale parameter used in Gaussian leaf models (can either be a scalar or a q x q matrix, where q is the dimensionality of the basis and is only >1 when leaf_model_int = 2). Calibrated internally as 1/num_trees, propagated along diagonal if needed for multivariate leaf models.

variance_forest_shape

Shape parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.

variance_forest_scale

Scale parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.

cutpoint_grid_size

Number of unique cutpoints to consider (default: 100)

Value

ForestModelConfig object

Examples

config <- createForestModelConfig(num_trees = 10, num_features = 5, num_observations = 100)
#> Warning: `feature_types` not provided, will be assumed to be numeric
#> Warning: `variable_weights` not provided, will be assumed to be equal-weighted