Slide 6
Slide 6 text
• A long list of classification metric + plots
• Accuracy
• Confusion matrix, FP, FN, TP, TN
• Precision, Recall, F1 score + Precision-Recall curve
• Lift chart + waterfall analysis
• AUC + ROC curve
Motivation
6
Choose the right metric after a classification model is built
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Achieved AUC 0.8
Achieved an Accuracy
0.98
Achieved high precision
but low recall
• Questions:
• Does a good looking metric truly reflect the reality?
• The chosen metric looks good, but does it help the business?
• How to explain AUC to stakeholder with little knowledge of ML?