make.classif.task

make.classif.task(mlr)

R Documentation

make.classif.task

Description

make.classif.task defines a classification task for a learner and a data set and is the starting point for further steps like training, predicting new data, resampling and tuning.

Usage

make.classif.task(learner, formula, data, weights, type)

Arguments

`learner`	[`character`] Specifies the learner. See the list below in the details section.
`formula`	[`formula`] A symbolic description of the model to be fitted.
`data`	[`data.frame`] A data frame containing the variables in the model.
`weights`	[`numeric`] An optional vector of weights to be used in the fitting process. Default is a weight of 1 for every case.
`type`	[`character`] Specifies the type of the predictions - either probabilities ("prob") or classes ("class"). Default is "class".

Details

make.classif.task already performs quite a few tasks: It tries to load the required package for the learner, sets up the learner to deal with a classification problem, gathers information about the features of the data set and the method, and compares whether they are compatible (e.g. some methods might not handle NAs or factors). And last but not least it might perform some data conversions in the data.frame, like coverting integer features to numerics or integer classes to factors, but will generally warn about this.

List of supported learning algorithms. The naming conventions are to add the package name as a prefix if a learner is implemented in different packages and the suffix ".classif" if it can handle more than a classification task.

adaboost

Boosting from adabag package

kknn.classif

k-Nearest Neighbor from kknn package

lda

Linear Discriminant Analysis from MASS package

logreg

Logistic Regression from stats package

mda

Mixture Discriminant Analysis from mda package

naiveBayes

Naive Bayes from e1071 package

qda

Quadratic Discriminant Analysis from MASS package

randomForest.classif

Random Forest from randomForest package

rda

Regularized Discriminant Analysis from klaR package

rpart.classif

Decision Tree from rpart package

kernlab.svm.classif

Support Vector Machines from kernlab package

Value

An object of class classif.task.

Examples

data(iris) 
# define a classification task for a decision tree (rpart) for the data set iris
ct <- make.classif.task("rpart.classif", data = iris, formula = Species ~.)

[Package mlr version 0.3.180 Index]