2025/09/14 by Ivan Petej, Petej, Ivan
Computer Science · #Machine Learning and Data Classification #Time Series Analysis and Forecasting #Anomaly Detection Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2509.11267
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.