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, ...)

## 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. |

## 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.

## 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.