makePreprocWrapper

[<code><a rd-options="" href="/link/Learner?package=mlr&version=2.10" data-mini-rdoc="mlr::Learner">Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.10" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code>.

learner

[<code>function(data, target, args)</code>]
Function to preprocess the data before training.
<code>target</code> is a string and denotes the target variable in <code>data</code>.
<code>args</code> is a list of further arguments and parameters to influence the
preprocessing.
Must return a <code>list(data, control)</code>, where <code>data</code> is the preprocessed
data and <code>control</code> stores all information necessary to do the preprocessing
before predictions.

train

[<code>function(data, target, args, control)</code>]
Function to preprocess the data before prediction.
<code>target</code> is a string and denotes the target variable in <code>data</code>.
<code>args</code> are the args that were passed to <code>train</code>.
<code>control</code> is the object you returned in <code>train</code>.
Must return the processed data.

predict

[<code><a rd-options="ParamHelpers" href="/link/ParamSet?package=mlr&version=2.10&to=ParamHelpers" data-mini-rdoc="ParamHelpers::ParamSet">ParamSet</a></code>]
Parameter set of <code><a rd-options="ParamHelpers" href="/link/LearnerParam?package=mlr&version=2.10&to=ParamHelpers" data-mini-rdoc="ParamHelpers::LearnerParam">LearnerParam</a></code> objects to describe the
parameters in <code>args</code>.
Default is empty set.

par.set

[<code>list</code>]
Named list of default values for params in <code>args</code> respectively <code>par.set</code>.
Default is empty list.

par.vals

Fuses a base learner with a preprocessing method. Creates a learner object, which can be
used like any other learner object, but which internally preprocesses the data as requested.
If the train or predict function is called on data / a task, the preprocessing is always performed automatically.

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

makePreprocWrapper function

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

[<code><a rd-options='ParamHelpers' href='ParamSet'>ParamSet</a></code>]
Parameter set of <code><a rd-options='ParamHelpers' href='LearnerParam'>LearnerParam</a></code> objects to describe the
parameters in <code>args</code>.
Default is empty set.

makePreprocWrapper: Fuse learner with preprocessing.

Description

Usage

Arguments

Value

See Also