Probability Calibration Trees [article]

Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer
2018 arXiv   pre-print
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Existing methods of calibrating probability estimates are applied globally, ignoring the potential for improvements by applying a more fine-grained model. We propose probability calibration trees, a modification of logistic model trees that identifies regions of the input space in which different probability calibration
more » ... odels are learned to improve performance. We compare probability calibration trees to two widely used calibration methods---isotonic regression and Platt scaling---and show that our method results in lower root mean squared error on average than both methods, for estimates produced by a variety of base learners.
arXiv:1808.00111v2 fatcat:sy54z3qbnbgy7hidvk3zlptoyu