| train(mlr) | R Documentation |
Given a learn.task train creates a model for the learning machine
which can be used for predictions on new data.
train(learn.task, subset, parset, vars)
learn.task |
[learn.task]Specifies learning task. |
subset |
[integer] An index vector specifying the training cases from the data contained in the learning task. By default the complete dataset is used. |
parset |
[list] Named list which contains the hyperparameters of the learner. Default is an empty list, which means no hyperparameters are specifically set and defaults of the underlying learner are used. |
vars |
[character] Vector of variable names to use in training the model. Default is to use all variables. |
An object of class wrapped.model containing the generated model of the underlying learner and the paramater and index set used for training.
predict, make.classif.task, make.regr.task
library(MASS)
train.inds <- seq(1,150,2)
test.inds <- seq(2,150,2)
ct <- make.classif.task("lda", data=iris, formula=Species~.)
cm <- train(ct, subset=train.inds)
ps <- predict(ct, cm, newdata=iris[test.inds,])
ct <- make.classif.task("kknn.classif", data=iris, formula=Species~.)
cm <- train(ct, subset=train.inds, parset=list(k=3))
ps <- predict(ct, cm, newdata=iris[test.inds,])