Glossary · Phase 3 · Machine learning
Confusion matrix
A table counting a classifier's predictions against the true answers: one row per true class, one column per predicted class. For yes/no it shows true and false positives and negatives.
A single accuracy number says how often the model is right. It doesn't say what it gets wrong. A confusion matrix does. It's a tally table, like a mail room keeping count of where letters should have gone and where they actually went.
Try it now
One score per class, turned into probabilities that add up to 1. Drag a score and watch every probability move.
Learn it properly
Multiclass classification · Softmax and cross-entropy for more than two classes. About 30 minutes.
Other terms from the same lesson
- majority baseline: The accuracy you get by always predicting the most common class. A model has to beat it to be worth anything.
- multiclass classification: Classification with more than two possible answers, such as sorting customers by their favorite product category.
- softmax regression: Logistic regression for more than two classes: one score per class, turned into probabilities by softmax.
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