AUC
Area Under the Curve - measures the area under the ROC curve, indicating classification model performance (1.0 is perfect).
Your route here
9 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Confusion Matrix ✓ understood
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
- Recall ✓ understood
The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.
- False Positive ✓ understood
Incorrectly predicted positive cases (Type I error) in classification.
- True Negative ✓ understood
Correctly predicted negative cases in classification.
- False Positive Rate ✓ understood
The proportion of negatives incorrectly classified as positive.
- ROC Curve ✓ understood
Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds.
- AUC · you are here ✓ understood