A stacked learner uses predictions of several base learners and fits
a super learner using these predictions as features in order to predict the outcome.
The following stacking methods are available:<p></p><p> [object Object],[object Object],[object Object]</p>

makeStackedLearner

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.

makeStackedLearner: Create a stacked learner object.

Description

Usage

Arguments