Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models.

Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called (ENIR). The method can be considered as an extension of BBQ [20], a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC) [27]. ENIR is designed to address the key limitation of IsoRegC which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be used with many existing classification models to generate accurate probabilistic predictions. We demonstrate the performance of ENIR on synthetic and real datasets for commonly applied binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is ( log ) time, where is the number of samples.