Reference 5 stops to get here

Sensitivity

Same as recall - the proportion of actual positives correctly identified.

Your route here

5 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. Sensitivity · you are here ✓ understood

Same as recall - the proportion of actual positives correctly identified.

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

  • Recall
  • True Positive Rate
  • Evaluation

Where it sits

Before this

Recall
Sensitivity

Leads to

Nothing yet: a destination in its own right.

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Evaluation Specificity The proportion of actual negatives correctly identified. Evaluation False Negative Positive cases that are incorrectly predicted as negative (Type II error) in classification. Evaluation ROC Curve Receiver Operating Characteristic curve - plots true positive rate vs false positive rate at various classification thresholds.