Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers

By Bianca Zadrozny, Charles Peter Elkan. 2001

1,118 citations recorded by the source corpus.

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Citations in the corpus

On Fairness and Calibration (2017, background): “It is not straightforward to encode the calibration constraint if the probabilities are assumed to be continuous, and post-processing calibration methods [31, 35] would break equal-cost constraints by modifying classifier scores.”
Obtaining Well Calibrated Probabilities Using Bayesian Binning (2015, methodology): “BBQ extends the simple histogram-binning calibration method (Zadrozny and Elkan 2001) by considering multiple binning models and their combination.”
Predicting good probabilities with supervised learning (2005, methodology): “Platt Scaling: a method for transforming SVM outputs from [−∞,+∞] to posterior probabilities (Platt, 1999) Isotonic Regression: the method used by Zadrozny and Elkan (2002; 2001) to calibrate predictions from boosted naive bayes, SVM, and decision tree models Platt Scaling is most effective when…”
Local Temperature Scaling for Probability Calibration (2020, background): “Non-parametric histogram binning [67] uses the average number of positive-class samples in each bin as the calibrated probability.”
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction (2019, methodology): “Examples of such methods include binning (Zadrozny and Elkan, 2001), isotonic regression (Zadrozny and Elkan, 2002), logistic scaling (Platt, 1999) and improvements thereof (Kull et al.”
An experimental comparison of performance measures for classification (2009, methodology): “Finally, in other works Zadrozny and Elkan (2001), different probabilistic measures (MSE, log-loss and profit) are used to evaluate different methods for calibrating classifiers.”
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019, methodology): “Examples of such methods include binning (Zadrozny and Elkan, 2001), isotonic regression (Zadrozny and Elkan, 2002), logistic scaling (Platt, 1999) and improvements thereof (Kull et al.”
Calibrating Deep Neural Networks using Focal Loss (2020, methodology): “Much work has been inspired by approaches that were not originally formulated in a deep learning context, such as Platt scaling [30], histogram binning [40], isotonic regression [41], and Bayesian binning and averaging [22, 21].”
Learning Decision Trees for Unbalanced Data (2008, background): “Various authors have implemented DKM as a decision tree splitting criterion and shown its improved performance on unbalanced datasets [9,11,12].”
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs (2023, methodology): “To achieve this, a variety of methods have been proposed, which can be broadly divided into scaling-based methods (Guo et al., 2017; Deng et al., 2023; Zhang et al., 2020) and binning-based methods (Zadrozny & Elkan, 2001; Zhang et al., 2020).”
Relational Dependency Networks (2007, background): “This indicates that the inaccuracies may be due to the naive Bayes independence assumption and its tendency to produce biased probability estimates (Zadrozny and Elkan, 2001).”