subsetTask

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.8" data-mini-rdoc="mlr::Task">Task</a></code>]
The task.

task

[<code>integer</code> | <code>logical(n)</code>]
Selected cases.
Default is all cases.

subset

[<code>character</code>]
Selected inputs. Note that target feature is always included in the
resulting task, you should not pass it here.
Default is all features.

features

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.

subsetTask: Subset data in task.

Description

Usage

Arguments

Value

See Also

Examples