Confusion matrix

Using scikit-learn's confusion_matrix() function, you can easily create your classifier's confusion matrix and gain a more nuanced understanding of its performance. It takes in two arguments: The actual labels of your test set - y_test - and your predicted labels.

The predicted labels of your Random Forest classifier from the previous exercise are stored in y_pred and were computed as follows:

y_pred = clf.predict(X_test)

Important note: sklearn, by default, computes the confusion matrix as follows:

Notice that the axes are the opposite of what you saw in the video. The metrics themselves remain the same, but keep this in mind when interpreting the table.