getTaskFormula

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.10" data-mini-rdoc="mlr::Task">Task</a></code> | <code><a rd-options="" href="/link/TaskDesc?package=mlr&version=2.10" data-mini-rdoc="mlr::TaskDesc">TaskDesc</a></code>]
Task or its description object.

[<code>character(1)</code>]
Left hand side of the formula.
Default is defined by task <code>x</code>.

target

[<code>logical(1)</code>]
Should the features (right hand side of the formula) be explicitly listed?
Default is <code>FALSE</code>, i.e., they will be represented as <code>&quot;.&quot;</code>.

explicit.features

[<code>environment</code>]
Environment of the formula.
Default is <code>parent.frame()</code>.

This is usually simply &#8220;&lt;target&gt; ~ .&#8221;.
For multilabel it is &#8220;&lt;target_1&gt; + ... + &lt;target_k&gt; ~ .&#8221;.

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

getTaskFormula function

[<code><a rd-options='' href='Task'>Task</a></code> | <code><a rd-options='' href='TaskDesc'>TaskDesc</a></code>]
Task or its description object.

getTaskFormula: Get formula of a task.

Description

Usage

Arguments

Value

See Also