intraining_estimations: Out-of-bag loss estimations

In tsensembler: Dynamic Ensembles for Time Series Forecasting

Description

A pipeline for retrieving out-of-bag loss estimations

Usage

intraining_estimations(train, test, form, specs, lfun, num_cores)

Arguments

train	train set from the training set;
test	test set from the training set;
form	formula;
specs	object of class model_specs-class. Contains the specifications of the base models.
lfun	loss function for metalearning. Defaults to ae – absolute error.
num_cores	A numeric value to specify the number of cores used to train base and meta models. num_cores = 1 leads to sequential training of models. num_cores > 1 splits the training of the base models across num_cores cores.

Value

A list containing two objects:

mloss

loss of base models in test

oob

out-of-bag test samples

Y_hat

predictions by base models

See Also

Other out-of-bag functions: intraining_predictions()

tsensembler documentation built on Oct. 27, 2020, 5:07 p.m.