Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
Machine Learning
The Evaluation of Predictive Learners: Some Theoretical and Empirical Results
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Journal of Artificial Intelligence Research
A bayesian metric for evaluating machine learning algorithms
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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We generalize an information-based reward function, introduced by Good (1952), for use with machine learners of classification functions. We discuss the advantages of our function over predictive accuracy and the metric of Kononenko and Bratko (1991). We examine the use of information reward to evaluate popular machine learning algorithms (e.g., C5.0, Naive Bayes, CaMML) using UCI archive datasets, finding that the assessment implied by predictive accuracy is often reversed when using information reward.