cv_control: cross validation set-up for model fitting functions

In jbrowell/ProbCast: Flexible Probabilistic Forecasting

cross validation set-up for model fitting functions

Description

This generates kfold indexes and vectors of reference values for the model fitting loop

Usage

cv_control(data, cv_folds = "kfold")

Arguments

data

A data.frame containing target and explanatory variables. May optionally contain a column with labeled folds and "Test" for test data. See cv_folds.

cv_folds

Control for cross-validation with various options, either: the column name of the fold index supplied in data. Using this option, if there is a fold called "Test", this will serve as test data and held out in the model training. an integer giving the number of cv evaluations to perform. Folds are constructed as block chunks. Default behaviour is 5 folds. NULL indicates that no cross validation should be performed and the returned model is trained on all data. vector of length==nrow(data) containing character or numeric fold labels

Details

....

Value

A named list containing the kfold indexes and the kfold loop reference values

Author(s)

Ciaran Gilbert, [e-mail]

jbrowell/ProbCast documentation built on July 20, 2024, 1:53 p.m.