### 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.

One of the most useful tools for evaluating the performance of any classification algorithm is the confusion matrix.

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