Data Mining pp 319-330 | Cite as

Classification Performance Evaluation

  • Florin Gorunescu
Part of the Intelligent Systems Reference Library book series (ISRL, volume 12)


A great part of this book presented the fundamentals of the classification process, a crucial field in data mining. It is now the time to deal with certain aspects of the way in which we can evaluate the performance of different classification (and decision) models. The problem of comparing classifiers is not at all an easy task. There is no single classifier that works best on all given problems, phenomenon related to the ”No-free-lunch” metaphor, i.e., each classifier (’restaurant’) provides a specific technique associated with the corresponding costs (’menu’ and ’price’ for it). It is hence up to us, using the information and knowledge at hand, to find the optimal trade-off.


Positive Predictive Value Negative Predictive Value False Negative Rate Testing Dataset Confusion Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Florin Gorunescu

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