Standard 8 stops to get here · leads to 1

ROC Curve

Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds.

Your route here

8 stops · basics first
  1. 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.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. 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.

  4. Confusion Matrix ✓ understood

    A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.

  5. Recall ✓ understood

    The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.

  6. False Positive ✓ understood

    Incorrectly predicted positive cases (Type I error) in classification.

  7. True Negative ✓ understood

    Correctly predicted negative cases in classification.

  8. False Positive Rate ✓ understood

    The proportion of negatives incorrectly classified as positive.

  9. ROC Curve · you are here ✓ understood

Where it sits

Before this

Recall False Positive Rate
ROC Curve

Leads to

AUC

Explore nearby

Evaluation AUC Area Under the Curve - measures the area under the ROC curve, indicating classification model performance (1.0 is perfect). Evaluation Precision-Recall Curve A curve showing the tradeoff between precision and recall at different thresholds. Evaluation Sensitivity Same as recall - the proportion of actual positives correctly identified. Evaluation Specificity The proportion of actual negatives correctly identified. Evaluation Calibration Ensuring predicted probabilities accurately reflect true likelihood of outcomes.