Standard 6 stops to get here · leads to 1

False Positive Rate

The proportion of negatives incorrectly classified as positive.

Your route here

6 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. False Positive ✓ understood

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

  6. True Negative ✓ understood

    Correctly predicted negative cases in classification.

  7. False Positive Rate · you are here ✓ understood

The proportion of negatives incorrectly classified as positive.

This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.

  • Specificity
  • ROC Curve
  • Evaluation

Where it sits

Before this

False Positive True Negative
False Positive Rate

Leads to

ROC Curve

Explore nearby

Evaluation Specificity The proportion of actual negatives correctly identified. Evaluation ROC Curve Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds. Evaluation AUC Area Under the Curve - measures the area under the ROC curve, indicating classification model performance (1.0 is perfect).