predictLearner: Predict new data with an R learner.
In guillermozbta/s2: Machine Learning in R

Description Usage Arguments Details Value

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.

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

`.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.

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.