The Basics: evaluating classifiers


Judging a classification model feels like it should be an easier task than judging a regression. After all, your prediction from a classification model can only either be right or wrong, while a prediction from a regression model can be more or less wrong, can have any level of error, high or low. Yet, judging a classification is not as simple as it may seem. There's more than one way for a classification to be right or to be wrong, and multiple ways to combine the different ways to be right and wrong into a unified metric. Of course, all these different metrics have different, frequently unintuitive names -- precision, recall, F1, ROC curves -- making the process seem a little forbidding from the outside.

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