Confusion Matrix
Updated at 2018-07-06 22:41
Confusion matrix is a visualization of the performance of a machine learning algorithm.
| Actual 0 | Actual 1 | |
|---|---|---|
| Predicted 0 | True Negative | False Negative |
| Predicted 1 | False Positive | True Positive |
Precision = TP / (TP+FP)
Recall = TP / (TP+FN)
Accuracy = (TP+TN) / (TP+TN+FP+FN)
F1 Score = (2 * Precision * Recall) / (Precision + Recall)
Precision describes how confident we are with positive predictions.
Recall describes how big portion of our positive predictions cover all actual positives.
For example:
sample of 27 animals — 8 cats, 6 dogs, and 13 rabbits
| Actual Cat | Actual Dog | Actual Rabbit | |
|---|---|---|---|
| Predicted Cat | 5 | 2 | 0 |
| Predicted Dog | 3 | 3 | 2 |
| Predicted Rabbit | 0 | 1 | 11 |
Table of confusion for cat class:
| Actual Cat | Actual Non-cat | |
|---|---|---|
| Predicted Cat | 5 TP | 2 FP (2+0) |
| Predicted Non-cat | 3 FN (3+0) | 17 TN (3+2+1+11) |
Table of confusion for dog class:
| Actual Dog | Actual Non-dog | |
|---|---|---|
| Predicted Dog | 3 TP | 5 FP (3+2) |
| Predicted Non-dog | 3 FN (2+1) | 16 TN (5+11) |
Table of confusion for rabbit class:
| Actual Rabbit | Actual Non-rabbit | |
|---|---|---|
| Predicted Rabbit | 11 TP | 1 FP (1+0) |
| Predicted Non-rabbit | 2 FN (2+0) | 13 TN (5+2+3+3) |
The final table of confusion would contain the average values for all classes combined:
| Actual 1 | Actual 0 | |
|---|---|---|
| Predicted 1 | 6.33 TP | 2.66 FP |
| Predicted 0 | 2.66 FN | 15.33 TN |
Precision = 6.33 / (6.33 + 2.66) = 0.70
Recall = 6.33 / (6.33 + 2.66) = 0.70
Accuracy = (6.33 + 15.33) / (6.33 + 15.33 + 2.66 + 2.66) = 0.80
F1 Score = (2 * 0.70 * 0.70) / (0.70 + 0.70) = 0.70