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.

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

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.

- .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.

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.