For Heads-of · Practitioner
Confusion Matrix
A table showing predicted vs. actual values for a classification task, enabling calculation of metrics like precision, recall, and false positive/negative rates.
- confusion matrix
- metrics
- evaluation
- classification
Structure
Rows show actual labels, columns show predicted labels. Diagonal entries are correct predictions, off-diagonal entries are errors, revealing specific failure modes.
Insights
A confusion matrix reveals whether a model tends to over-predict or under-predict certain classes, guiding interpretation of performance metrics.
Fairness Analysis
Separate confusion matrices for different demographic groups can reveal disparities: the model may have different error rates across groups, indicating fairness issues.