Standard 3 stops to get here · leads to 7
Confusion Matrix
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
Your route here
3 stops · basics first
- 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.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- 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.
- Confusion Matrix · you are here ✓ understood
Where it sits
Before this
Classification Confusion Matrix
Explore nearby
Evaluation True Positive Correctly predicted positive cases in classification. Evaluation False Positive Incorrectly predicted positive cases (Type I error) in classification. Evaluation True Negative Correctly predicted negative cases in classification. Evaluation False Negative Positive cases that are incorrectly predicted as negative (Type II error) in classification. Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive. Evaluation Recall The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.