getTaskData: Extract data in task.
In shuodata/mlr-master: Machine Learning in R

Description Usage Arguments Value See Also Examples

Useful in trainLearner when you add a learning machine to the package.

1 2	getTaskData(task, subset = NULL, features, target.extra = FALSE, recode.target = "no")

`task`	[`Task`] The task.
`subset`	[`integer` \| `logical`] Selected cases. Either a logical or an index vector. By default 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.
`target.extra`	[`logical(1)`] Should target vector be returned separately? If not, a single data.frame including the target columns is returned, otherwise a list with the input data.frame and an extra vector or data.frame for the targets. Default is `FALSE`.
`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 `Surv` for the format specification. Default for both binary classification and survival is “no” (do nothing).

Either a data.frame or a list with data.frame data and vector target.

Other task: getTaskClassLevels, getTaskCosts, getTaskDesc, getTaskFeatureNames, getTaskFormula, getTaskId, getTaskNFeats, getTaskSize, getTaskTargetNames, getTaskTargets, getTaskType, subsetTask

library("mlbench")
data(BreastCancer)

df = BreastCancer
df$Id = NULL
task = makeClassifTask(id = "BreastCancer", data = df, target = "Class", positive = "malignant")
head(getTaskData)
head(getTaskData(task, features = c("Cell.size", "Cell.shape"), recode.target = "-1+1"))
head(getTaskData(task, subset = 1:100, recode.target = "01"))