makeFilter

[<code>character(1)</code>]
Identifier for the filter.

name

[<code>character(1)</code>]
Short description of the filter.

desc

[<code>character(1)</code>]
Source package where the filter is implemented.

[<code>character</code>]
Task types supported.

supported.tasks

[<code>character</code>]
Feature types supported.

supported.features

[<code>function(task, nselect, ...</code>]
Function which takes a task and returns a named numeric vector of scores,
one score for each feature of <code>task</code>.
Higher scores mean higher importance of the feature.
At least <code>nselect</code> features must be calculated, the remaining may be
set to <code>NA</code> or omitted, and thus will not be selected.
the original order will be restored if necessary.


Creates and registers custom feature filters. Implemented filters
can be listed with <code><a rd-options="" href="/link/listFilterMethods?package=mlr&version=2.8" data-mini-rdoc="mlr::listFilterMethods">listFilterMethods</a></code>.


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.

makeFilter: Create a feature filter.

Description

Usage

Arguments

Value