getStackedBaseLearnerPredictions

([WrappedModel`) Wrapped model, result of train.

model

(<a rd-options="" href="/link/data.frame?package=mlr&version=2.12.1" data-mini-rdoc="mlr::data.frame">data.frame</a>)
New observations, for which the predictions using the specified base learners should be returned.
Default is <code>NULL</code> and extracts the base learner predictions that were made during the training.

newdata

Returns the predictions for each base learner.

Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.

Bernd Bischl

Machine Learning in R

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Patrick Schratz

Steve Bronder

getStackedBaseLearnerPredictions function

(<a rd-options='' href='data.frame'>data.frame</a>)
New observations, for which the predictions using the specified base learners should be returned.
Default is <code>NULL</code> and extracts the base learner predictions that were made during the training.

Returns the predictions for each base learner. — getStackedBaseLearnerPredictions

Returns the predictions for each base learner.

getStackedBaseLearnerPredictions: Returns the predictions for each base learner.

Description

Usage

Arguments

Details