Standard 6 stops to get here

Specificity

The proportion of actual negatives correctly identified.

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. True Negative ✓ understood

    Correctly predicted negative cases in classification.

  6. False Positive ✓ understood

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

  7. Specificity · you are here ✓ understood

The proportion of actual negatives correctly identified.

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

  • True Negative Rate
  • Evaluation
  • ROC Curve

Where it sits

Before this

True Negative False Positive
Specificity

Leads to

Nothing yet: a destination in its own right.

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Evaluation Sensitivity Same as recall - the proportion of actual positives correctly identified. Evaluation False Positive Rate The proportion of negatives incorrectly classified as positive. Evaluation ROC Curve Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds.