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Wiki/Machine Learning/Model Validation and Evaluation.md
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@@ -73,4 +73,4 @@ When using it in this context a value of 1 represents a perfect classifier (mode
### ROC and AUC ### ROC and AUC
ROC stands for the Receiver-operating characteristic curve. The graph is found by plotting the true positive rate on the y-axis and the false positive rate on the x-axis as the threshold changes. This is a parametric graph with the threshold as the parameter. ROC stands for the Receiver-operating characteristic curve. The graph is found by plotting the true positive rate on the y-axis and the false positive rate on the x-axis as the threshold changes. This is a parametric graph with the threshold as the parameter.
![[rocCurve.excalidraw]] ![[rocCurve.excalidraw]]
A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives) A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives). A straight line from $(0, 0)$ to $(1, 1)$ repres