A calibrated classifier is one where the predicted probability of a class closely matches the
rate at which that class occurs, e.g. for data points which are assigned a predicted probability
of class A of .8, approximately 80 percent of such points should belong to class A if the classifier
is well calibrated. This is estimated empirically by grouping data points with similar predicted
probabilities for each class, and plotting the rate of each class within each bin against the
predicted probability bins.

generateCalibrationData

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.

generateCalibrationData: Generate classifier calibration data.

Description

Usage

Arguments

References

See Also