Building Machine Learning Systems with Python - Third Edition

Using a confusion matrix to measure accuracy in multiclass problems

With multiclass problems, we shouldn't just be interested in how well we manage to correctly classify the genres. We should also look into which genres we confuse with each other. This can be done with the appropriately named confusion matrix, which you may have noticed is part of the training procedure:

>>> cm = confusion_matrix(y_test, y_pred)

If we print out the confusion matrix, we would see something like the following:

 [[26 1 2 0 0 2] [ 4 7 5 0 5 3] [ 1 2 14 2 8 3] [ 5 4 7 3 7 5] [ 0 0 10 2 10 12] [ 1 0 4 0 13 12]]

This is the distribution of labels that the classifier predicted for the test set for every genre. The diagonal represents the correct classifications. ...