R: Predict new data with an R learner.

predictLearner {mlr} | R Documentation

## Predict new data with an R learner.

### Description

Mainly for internal use. Predict new data with a fitted model. You have to implement this method if you want to add another learner to this package.

### Usage

    predictLearner(.learner, .model, .newdata, ...)

### Arguments

`.learner` | (RLearner) Wrapped learner.
`.model` | (WrappedModel) Model produced by training.
`.newdata` | (data.frame) New data to predict. Does not include target column.
`...` | (any) Additional parameters, which need to be passed to the underlying predict function.

### Details

Your implementation must adhere to the following: Predictions for the observations in `.newdata` must be made based on the fitted model (`.model$learner.model`). All parameters in `...` must be passed to the underlying predict function.

### Value

- For classification: Either a factor with class labels for type “response” or, if the learner supports this, a matrix of class probabilities for type “prob”. In the latter case the columns must be named with the class labels.

- For regression: Either a numeric vector for type “response” or, if the learner supports this, a matrix with two columns for type “se”. In the latter case the first column contains the estimated response (mean value) and the second column the estimated standard errors.

- For survival: Either a numeric vector with some sort of orderable risk for type “response” or, if supported, a numeric vector with time dependent probabilities for type “prob”.

- For clustering: Either an integer with cluster IDs for type “response” or, if supported, a matrix of membership probabilities for type “prob”.

- For multilabel: A logical matrix that indicates predicted class labels for type “response” or, if supported, a matrix of class probabilities for type “prob”. The columns must be named with the class labels.

[Package mlr version 2.12.1 Index]