Interpreting the ROC Curve in H2O Driverless AI
- Predictive AI
- H2O Driverless AI
This lesson walks through how to read the ROC curve in H2O Driverless AI, a graph that measures classification accuracy through the area under its curve. It covers the three marked points, best F1, best MCC, and best AUC, and shows how hovering over a point on the curve opens a confusion matrix comparing predicted versus actual classes. Using a credit score example where the model correctly predicts around 90 percent of cases, the lesson closes with questions to ask about the remaining errors, including which class matters more to the business case.
Key takeaways
- How the area under the ROC curve measures a classification model's overall predictive accuracy
- The three marked points on the ROC curve, best F1, best MCC, and best AUC, and what hovering over one shows
- How a confusion matrix compares predicted versus actual classes for a chosen point on the curve
- Why examining the secondary diagonal errors helps decide whether outlier observations need separate treatment or removal
- What questions to ask about which class matters more to your business case when errors remain
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