erf_cv: Cross-validation for an extremal random forest (ERF)

In nicolagnecco/erf: Extreme Random Forest

Cross-validation for an extremal random forest (ERF)

Description

Fits an extremal random forest (ERF) with cross-validation.

Usage

erf_cv(
  X,
  Y,
  min.node.size = c(5, 40, 100),
  lambda = c(0, 0.001, 0.01),
  intermediate_estimator = c("grf", "neural_nets"),
  intermediate_quantile = 0.8,
  nfolds = 5,
  nreps = 3,
  seed = NULL
)

Arguments

X	Numeric matrix of predictors, where each row corresponds to an observation and each column to a predictor.
Y	Numeric vector of responses.
min.node.size	Vector with minimum number of observations in each tree leaf used to fit the similarity weights (see also grf::quantile_forest()). Nodes with size smaller than min.node.size can occur, as in the original randomForest package. Default is c(5, 40, 100).
lambda	Vector with penalties for the shape parameter used in the weighted likelihood. Default is c(0, 0.001, 0.01).
intermediate_estimator	A character specifying the estimator used to fit the intermediate threshold. Options available are: grf, see grf::quantile_forest(). neural_nets, (coming soon).
intermediate_quantile	Intermediate quantile level, used to predict the intermediate threshold. For further information see \insertCitemerg2020;textualerf. Default is 0.8.
nfolds	Number of folds in the cross-validation scheme. Default is 5.
nreps	Number of times nfolds cross-validation is repeated. Default is 3.
seed	Random seed to reproduce the fold splits. Default is NULL.

Value

An object with S3 class "erf_cv". It is a named list with the following elements:

scores	A tibble with columns: min.node.size, lambda, cvm (mean cross-validated error).
erf	A fitted "erf" object on the full data using the optimal min.node.size and lambda.

nicolagnecco/erf documentation built on Dec. 4, 2024, 1:54 a.m.