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. K. McCallum and K. Nigamy. Employing EM and Pool-Based Active Learning for Text Classification. In Int. Conf. Mach. Learn., pages 359–367, 1998.