Protected Probabilistic Classification Library
This addresses the issue of maintaining classifier reliability when data distributions change, which is crucial for batch and online learning applications, though it appears incremental as it builds on existing calibration methods.
The paper tackles the problem of calibrating probabilistic classifiers under dataset shift, introducing a new Python package that demonstrates promising empirical results against existing post-hoc calibration methods in binary and multi-class settings.
This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets.