Too confused of the confusion matrix?
Let me bring some clarity into this topic!
Let’s take the example from Precision and Recall:
y_true = ["dog", "dog", "non-dog", "non-dog", "dog", "dog"] y_pred = ["dog", "non-dog", "dog", "non-dog", "dog", "non-dog"]
When we look at the prediction we can count the correct and incorrect classifications:
- dog correctly classified as dog: 2 times (True Positive)
- non-dog incorrectly classified as dog: 1 time (False Positive)
- dog incorrectly classified as non-dog: 2 times (False Negative)
- non-dog correctly classified as non-dog: 1 time (True Negative)
When we visualize these results in a matrix we already have the confusion matrix:
sklearn
We can calculate the confusion matrix with sklearn in a very simple manner
from sklearn.metrics import confusion_matrix print(confusion_matrix(y_true, y_pred, labels=["dog", "non-dog"]))
the output is:
[[2 2] [1 1]]
which can be indeed confusing because the matrix is transposed. In contrast to our matrix from above the columns are the prediction and the rows are the actual values:
And that’s all – if you just have a binary classifier.
Multi-label classifier
So what happens, when your classifier can decide between three outcomes, say dog, cat and rabbit? (You can generate the test data with numpy random choice)
y_true = ['rabbit', 'dog', 'rabbit', 'cat', 'cat', 'cat', 'cat', 'dog', 'cat'] y_pred = ['rabbit', 'rabbit', 'dog', 'cat', 'dog', 'rabbit', 'dog', 'cat', 'dog'] cm = confusion_matrix(y_true, y_pred, labels=["dog", "rabbit", "cat"])
[[0 1 1] [1 1 0] [3 1 1]]
