Confusion Matrix Explained

This slide deck explains what a confusion matrix is and how to interpret it.

Transcript

1. Confusion Matrix Explained Samuel Bohman
2. What is a Confusion Matrix? A common method for describing the performance of a classification model consisting of true positives, true negatives, false positives, and false negatives. It is called a confusion matrix because it shows how confused the model is between the classes.
3. True Positives Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0 The model correctly classified 50 apples and 50 oranges.
4. True Negatives for Apple The model correctly classified 75 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
5. True Negatives for Orange The model correctly classified 105 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
6. True Negatives for Pear The model correctly classified 115 cases as not belonging to class pear. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
7. False Positives for Apple The model incorrectly classified 15 cases as apples. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
8. False Positives for Orange The model incorrectly classified 10 cases as oranges. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
9. False Positives for Pear The model incorrectly classified 70 cases as pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
10. False Negatives for Apple The model incorrectly classified 55 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
11. False Negatives for Orange The model incorrectly classified 30 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
12. False Negatives for Pear The model incorrectly classified 10 cases as not belonging to class pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0