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False Negative

Positive cases that are incorrectly predicted as negative (Type II error) in classification.

Your route here

4 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 Negative · you are here ✓ understood

Positive cases that are incorrectly predicted as negative (Type II error) in classification.

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

  • Confusion Matrix
  • Recall
  • Type II Error

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Confusion Matrix
False Negative

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Evaluation Recall The proportion of true positives among all actual positives - measures how many actual positives were correctly identified. Evaluation False Positive Incorrectly predicted positive cases (Type I error) in classification. Evaluation Sensitivity Same as recall - the proportion of actual positives correctly identified. Evaluation True Positive Correctly predicted positive cases in classification.