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 an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

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

Description

Usage

Arguments

Value

See Also