Abstract
Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate “black boxes”—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).
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Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)
Provost, F.J., Fawcett, T.: Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Knowledge Discovery and Data Mining, 43–48 (1997)
Rheingans, P., desJardins, M.: Visualizing high-dimensional predictive model quality. In: Proceedings of IEEE Visualization 2000, pp. 493–496 (2000)
Russell, S., Norvig, P.: Artificial Intelligence. Prentice-Hall, Englewood Cliffs (1995)
Smyth, P.: Model selection for probabilistic clustering using cross-validated likelihood. Statistics and Computing, 63–72 (2000)
Thearling, K., et al.: Visualizing Data Mining Models. In: Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, San Francisco (2001)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
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© 2003 Springer-Verlag Berlin Heidelberg
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Frank, E., Hall, M. (2003). Visualizing Class Probability Estimators. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_17
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DOI: https://doi.org/10.1007/978-3-540-39804-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20085-7
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