Returns the optimal hyperparameters and optimization path after training or benchmarking.

getTuneResult

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.

getTuneResult: Returns the optimal hyperparameters and optimization path after training or benchmarking.

Description

Usage

Arguments

Value

See Also