Predicting good probabilities with supervised learning¶
Niculescu-Mizil, A., & Caruana, R. (2005). Predicting good probabilities with supervised learning. Proceedings of the 22nd International Conference on Machine Learning, 625-632.
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Primes¶
- Calibration
- The calibration procedure transfers across domains: spectrometer calibration, probability calibration, personnel evaluation calibration, and sensor calibration all follow the same structural template, as Niculescu-Mizil and Caruana (2005) demonstrated by comparing calibration methods across heterogeneous supervised-learning algorithms.
This sourceEmpirical comparison of the calibration of seven supervised-learning algorithms (SVMs, neural nets, decision trees, bagged/boosted trees, boosted stumps, memory-based, naive Bayes) and of Platt scaling vs isotonic regression — showing the same calibration template transfers across ML substrates.
- The calibration procedure transfers across domains: spectrometer calibration, probability calibration, personnel evaluation calibration, and sensor calibration all follow the same structural template, as Niculescu-Mizil and Caruana (2005) demonstrated by comparing calibration methods across heterogeneous supervised-learning algorithms.
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