train: Train a learning algorithm.
In shuodata/mlr-master: Machine Learning in R

Description Usage Arguments Value See Also Examples

Given a Task, creates a model for the learning machine which can be used for predictions on new data.

1	train(learner, task, subset, weights = NULL)

`learner`	[`Learner` \| `character(1)`] The learner. If you pass a string the learner will be created via `makeLearner`.
`task`	[`Task`] The task.
`subset`	[`integer` \| `logical`] Selected cases. Either a logical or an index vector. By default all observations are used.
`weights`	[`numeric`] Optional, non-negative case weight vector to be used during fitting. If given, must be of same length as `subset` and in corresponding order. By default `NULL` which means no weights are used unless specified in the task (`Task`). Weights from the task will be overwritten.

[WrappedModel].

predict.WrappedModel

training.set = sample(seq_len(nrow(iris)), nrow(iris) / 2)

## use linear discriminant analysis to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.lda", method = "mle")
mod = train(learner, task, subset = training.set)
print(mod)

## use random forest to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.rpart", minsplit = 7, predict.type = "prob")
mod = train(learner, task, subset = training.set)
print(mod)