plotFilterValues

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

fvalues

[<code>character(1)</code>]
Sort features like this.
&#8220;dec&#8221; = decreasing, &#8220;inc&#8221; = increasing, &#8220;none&#8221; = no sorting.
Default is decreasing.

sort

[<code>integer(1)</code>]
Number of features (maximal) to show.
Default is 20.

n.show

[<code>logical(1)</code>]
Colors for factor and numeric features.
<code>FALSE</code> means no colors.
Default is <code>FALSE</code>.

feat.type.cols

[<code>integer()</code>]
Number of rows and columns for facetting. Default for both is <code>NULL</code>.
In this case ggplot&#39;s <code>facet_wrap</code> will choose the layout itself.

facet.wrap.nrow, facet.wrap.ncol

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

plotFilterValues function

[<code><a rd-options='' href='FilterValues'>FilterValues</a></code>]
Filter values.

plotFilterValues: Plot filter values using ggplot2.

Description

Usage

Arguments

Value

See Also

Examples