skactiveml.pool.average_kl_divergence#
- skactiveml.pool.average_kl_divergence(probas, eps=1e-07)[source]#
Calculates the average Kullback-Leibler (KL) divergence for measuring the level of disagreement in QueryByCommittee.
- Parameters
- probasarray-like of shape (n_estimators, n_samples, n_classes)
The probability estimates of all estimators, samples, and classes.
- epsfloat > 0, optional (default=1e-7)
Minimum probability threshold to compute log-probabilities.
- Returns
- scoresnp.ndarray, shape (n_samples,)
The Kullback-Leibler (KL) divergences.
References
- 1
A. McCallum and K. Nigam. Employing EM in pool-based active learning for text classification. In International Conference on Machine Learning, pages 359-367, 1998.