Landmark 4 stops to get here · leads to 2

False Positive

Incorrectly predicted positive cases (Type I 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 Positive · you are here ✓ understood

Picture it

PREDICTEDpositivenegativeACTUALpositivenegativeTP42FN6FP8TN44precision = TP / (TP + FP)= 42 / 50 = 0.84recall = TP / (TP + FN)= 42 / 48 = 0.88
The FP cell counts negatives the model wrongly called positive; notice it appears in precision = TP/(TP+FP), pulling it down.

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

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

  • Confusion Matrix
  • Precision
  • Type I Error

Where it sits

Before this

Confusion Matrix
False Positive

Leads to

False Positive Rate Specificity

Explore nearby

Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive. Evaluation False Negative Positive cases that are incorrectly predicted as negative (Type II error) in classification. Evaluation False Positive Rate The proportion of negatives incorrectly classified as positive. Evaluation True Positive Correctly predicted positive cases in classification.