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Confusion Matrix
A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.
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- 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
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Classification Confusion Matrix
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Evaluation Type I and Type II Errors The two ways a yes-or-no decision can be wrong: a Type I error is a false alarm (false positive), a Type II error is a miss (false negative). Evaluation True Positive Correctly predicted positive cases in classification. Evaluation False Positive A case the model labels positive that is actually negative, such as a legitimate email sent to spam. Statisticians call it a Type I error. 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 Of everything a model flagged as positive, the fraction that really was positive: TP / (TP + FP). It answers "when the model says yes, how often is it right?" Evaluation Recall The share of actual positives a model correctly identifies, TP / (TP + FN); also called sensitivity or the true positive rate.