makeWrappedModel

WrappedModel

(<a rd-options="" href="/link/Learner?package=mlr&version=2.15.0" data-mini-rdoc="mlr::Learner">Learner</a> | <code>character(1)</code>)
The learner.
If you pass a string the learner will be created via <a rd-options="" href="/link/makeLearner?package=mlr&version=2.15.0" data-mini-rdoc="mlr::makeLearner">makeLearner</a>.

learner

learner.model

<a rd-options="" href="/link/TaskDesc?package=mlr&version=2.15.0" data-mini-rdoc="mlr::TaskDesc">TaskDesc</a>
Task description object.

task.desc

(<a rd-options="" href="/link/integer?package=mlr&version=2.15.0" data-mini-rdoc="mlr::integer">integer</a> | <a rd-options="" href="/link/logical?package=mlr&version=2.15.0" data-mini-rdoc="mlr::logical">logical</a> | <code>NULL</code>)
Selected cases. Either a logical or an index vector.
By default <code>NULL</code> if all observations are used.

subset

(<a rd-options="" href="/link/character?package=mlr&version=2.15.0" data-mini-rdoc="mlr::character">character</a>)
Features used for training.

features

(named <a rd-options="" href="/link/list?package=mlr&version=2.15.0" data-mini-rdoc="mlr::list">list</a> of <a rd-options="" href="/link/character?package=mlr&version=2.15.0" data-mini-rdoc="mlr::character">character</a>)
Levels of factor variables (features and potentially target) in training data.
Named by variable name, non-factors do not occur in the list.

factor.levels

(<code>numeric(1)</code>)
Computation time for model fit in seconds.

time

Result from <a rd-options="" href="/link/train?package=mlr&version=2.15.0" data-mini-rdoc="mlr::train">train</a>.
It internally stores the underlying fitted model,
the subset used for training, features used for training, levels of factors in the
data set and computation time that was spent for training.
Object members: See arguments.
The constructor <code>makeWrappedModel</code> is mainly for internal use.

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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

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

Steve Bronder

Alexander Engelhardt

Christoph Molnar

makeWrappedModel function

(<a rd-options='' href='Learner'>Learner</a> | <code>character(1)</code>)
The learner.
If you pass a string the learner will be created via <a rd-options='' href='makeLearner'>makeLearner</a>.

<a rd-options='' href='TaskDesc'>TaskDesc</a>
Task description object.

(<a rd-options='' href='integer'>integer</a> | <a rd-options='' href='logical'>logical</a> | <code>NULL</code>)
Selected cases. Either a logical or an index vector.
By default <code>NULL</code> if all observations are used.

(<a rd-options='' href='character'>character</a>)
Features used for training.

(named <a rd-options='' href='list'>list</a> of <a rd-options='' href='character'>character</a>)
Levels of factor variables (features and potentially target) in training data.
Named by variable name, non-factors do not occur in the list.

Result from <a rd-options='' href='train'>train</a>.
It internally stores the underlying fitted model,
the subset used for training, features used for training, levels of factors in the
data set and computation time that was spent for training.
Object members: See arguments.
The constructor <code>makeWrappedModel</code> is mainly for internal use.

makeWrappedModel: Induced model of learner.

Description

Usage

Arguments

Value