getTaskTargets

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.10" data-mini-rdoc="mlr::Task">Task</a></code>]
The task.

task

[<code>character(1)</code>] 
Should target classes be recoded? Only for binary classification.
Possible are &#8220;no&#8221; (do nothing), &#8220;01&#8221;, and &#8220;-1+1&#8221;.
In the two latter cases the target vector is converted into a numeric vector.
The positive class is coded as +1 and the negative class either as 0 or -1.
Default is &#8220;no&#8221;.

recode.target

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.

getTaskTargets: Get target data of task.

Description

Usage

Arguments

Value

See Also

Examples