Possible prediction types are:
Classification: Labels or class probabilities (including labels).
Regression: Numeric or response or standard errors (including numeric response).
Survival: Linear predictor or survival probability.<p></p><p>For complex wrappers the predict type is usually also passed down the
encapsulated learner in a recursive fashion.</p>

setPredictType

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

setPredictType: Set the type of predictions the learner should return.

Description

Usage

Arguments

Value

See Also