getFunctionalFeatures: Get only functional features from a task or a data.frame.

In mlr: Machine Learning in R

Get only functional features from a task or a data.frame.

Description

The parameters “subset”, “features”, and “recode.target” are ignored for the data.frame method.

Usage

getFunctionalFeatures(object, subset = NULL, features, recode.target = "no")

## S3 method for class 'Task'
getFunctionalFeatures(object, subset = NULL, features, recode.target = "no")

## S3 method for class 'data.frame'
getFunctionalFeatures(object, subset = NULL, features, recode.target = "no")

Arguments

object	(Task/data.frame) Object to check on.
subset	(integer | logical | NULL) Selected cases. Either a logical or an index vector. By default NULL if all observations are used.
features	(character | integer | logical) Vector of selected inputs. You can either pass a character vector with the feature names, a vector of indices, or a logical vector. In case of an index vector each element denotes the position of the feature name returned by getTaskFeatureNames. Note that the target feature is always included in the resulting task, you should not pass it here. Default is to use all features.
recode.target	(character(1)) Should target classes be recoded? Supported are binary and multilabel classification and survival. Possible values for binary classification are “01”, “-1+1” and “drop.levels”. 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”. “drop.levels” will remove empty factor levels in the target column. In the multilabel case the logical targets can be converted to factors with “multilabel.factor”. For survival, you may choose to recode the survival times to “left”, “right” or “interval2” censored times using “lcens”, “rcens” or “icens”, respectively. See survival::Surv for the format specification. Default for both binary classification and survival is “no” (do nothing).

Value

Returns a data.frame containing only the functional features.

mlr documentation built on June 22, 2024, 10:51 a.m.