Database Reference
In-Depth Information
Evaluating the performance of
classification models
When we make predictions using our model, as we did earlier, how do we know whether
the predictions are good or not? We need to be able to evaluate how well our model per-
forms. Evaluation metrics commonly used in binary classification include prediction accur-
acy and error, precision and recall, and area under the precision-recall curve, the
receiver
operating characteristic
(
ROC
) curve,
area under ROC curve
(
AUC
), and F-measure.
