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This chapter looks at the use of true and false positive and negative classifications as a better way of measuring the performance of a classifier than predictive accuracy alone. Other performance measures can be derived from these four basic ones, including true positive rate (or hit rate), false positive rate (or false alarm rate), precision, accuracy and F1 score.
The values of true positive rate and false positive rate are often represented diagrammatically by a ROC graph. Joining the points on a ROC graph to form a ROC curve can often give insight into the best way of tuning a classifier. A Euclidean distance measure of the difference between a given classifier and the performance of a hypothetical perfect classifier is described.
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- Measuring the Performance of a Classifier
Prof. Max Bramer
- Springer London
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