7.3.5. mclearn.heuristics.qbb_kl_h

mclearn.heuristics.qbb_kl_h(X_training_candidates, **kwargs)

Return the candidate with the largest average KL divergence from the mean.

We first use bagging to train k classifiers. We then choose the candidate that has the largest Kullback–Leibler divergence from the average.

Parameters:X_training_candidates (array) – The feature matrix of the potential training candidates. Returns:best_candidate – The index of the best candidate. Return type:int