06-auc.md

4.6 ROC AUC

Slides

Notes

The Area under the ROC curves can tell us how good is our model with a single value. The AUROC of a random model is 0.5, while for an ideal one is 1.

In other words, AUC can be interpreted as the probability that a randomly selected positive example has a greater score than a randomly selected negative example.

Classes and methods:

• auc(x, y) - sklearn.metrics class for calculating area under the curve of the x and y datasets. For ROC curves x would be false positive rate, and y true positive rate.
• roc_auc_score(x, y) - sklearn.metrics class for calculating area under the ROC curves of the x false positive rate and y true positive rate datasets.
• randint(x, y, size=z) - np.random class for generating random integers from the “discrete uniform” distribution; from x (inclusive) to y (exclusive) of size z.

The entire code of this project is available in this jupyter notebook.

Add notes from the video (PRs are welcome)

⚠️	The notes are written by the community. If you see an error here, please create a PR with a fix.

• Notes from Peter Ernicke

Navigation

• Machine Learning Zoomcamp course
• Session 4: Evaluation Metrics for Classification
• Previous: ROC Curves
• Next: Cross-Validation