R: Predict new data.

predict.WrappedModel {mlr} | R Documentation

## Predict new data.

### Description

Predict the target variable of new data using a fitted model. What is stored exactly in the (Prediction) object depends on the `predict.type` setting of the Learner. If `predict.type` was set to “prob” probability thresholding can be done calling the setThreshold function on the prediction object.

The row names of the input `task` or `newdata` are preserved in the output.

### Usage

    ## S3 method for class 'WrappedModel'
    predict(object, task, newdata, subset = NULL, ...)

### Arguments

`object` | (WrappedModel) Wrapped model, result of train.
`task` | (Task) The task. If this is passed, data from this task is predicted.
`newdata` | (data.frame) New observations which should be predicted. Pass this alternatively instead of `task`.
`subset` | (integer | logical | `NULL`) Selected cases. Either a logical or an index vector. By default `NULL` if all observations are used.
`...` | (any) Currently ignored.

### Value

(Prediction).

### See Also

Other predict: `asROCRPrediction()`, `getPredictionProbabilities()`, `getPredictionResponse()`, `getPredictionTaskDesc()`, `setPredictThreshold()`, `setPredictType()`

### Examples

    # train and predict
    train.set = seq(1, 150, 2)
    test.set = seq(2, 150, 2)
    model = train("classif.lda", iris.task, subset = train.set)
    p = predict(model, newdata = iris, subset = test.set)
    print(p)
    predict(model, task = iris.task, subset = test.set)

    # predict now probabiliies instead of class labels
    lrn = makeLearner("classif.lda", predict.type = "prob")
    model = train(lrn, iris.task, subset = train.set)
    p = predict(model, task = iris.task, subset = test.set)
    print(p)
    getPredictionProbabilities(p)

[Package mlr version 2.19.0 Index]