R: Train a learning algorithm.

train {mlr} | R Documentation

## Train a learning algorithm.

### Description

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

### Usage

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

### Arguments

`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 | `NULL`) Selected cases. Either a logical or an index vector. By default `NULL` if 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.

### Value

(WrappedModel).

### See Also

predict.WrappedModel

### Examples

    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)

[Package mlr version 2.15.0 Index]