How would you evaluate a Classification model using ROC/AUC?
The ROC curve is produced by plotting the False Positive Rate (FPR) on the x-axis and the True Positive Rate (TPR) on the y-axis for all decision rules.
The ROC curve is produced by plotting the False Positive Rate (FPR) on the x-axis and the True Positive Rate (TPR) on the y-axis for all decision rules.
False Positive Rate measures the proportion of actual negative observations that were predicted to be positive.
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