Confusion Matrix

#machine-learning #interview #evaluation

Confusion matrix is the summary of most of the evaluation metrics.

To learn more about each of the evaluation metrics:

  1. True Positive Rate
  2. True Negative Rate
  3. False Positive Rate
  4. False Negative Error
  5. Type 1 Error vs. Type 2 Error
  6. Precision
  7. Recall
  8. Sensitivity
  9. Specificity
  10. Accuracy
  11. ROC Curve, AUC Score - Calculated from TPR, FPR
  12. Precision Recall Curve (PRC), Area Under Precision Recall Curve (AUPRC) - Calculated from Precision, Recall
  13. F1 Score, F-Beta Score - Calculated from Precision, Recall

Lets look at the confusion matrix figure,

References

Related Notes