Evaluation Metrics II
Understand the AUC ROC curve and its role in evaluating classification models. Learn how to interpret true positive rate and false positive rate, and use these metrics to choose an optimal threshold for model predictions, illustrated by a practical Alzheimer’s biomarker example.
We'll cover the following...
3. AUC-ROC curve
AUC (area under the curve) - ROC (receiver operating characteristics) curve is a performance measurement for a classification model at various classification threshold settings. Basically, it is a probability curve that tells us how well the model is capable of distinguishing between classes. The higher the AUC value of our probability curve, the better the model is at predicting zeros as zeros and ones as ones.
What do we mean by various threshold settings?
Say we set the threshold to 0.9. This means that if for any given sample our trained model predicts a value higher than 0.9, our output class will be predicted as a positive ...