Creating a confusion matrix with cvms

When inspecting a classification model's performance, a confusion matrix tells you the distribution of the predictions and targets. For each combination, we can count how many times the model made that prediction for an observation with that target. This is often more useful than the various metrics, as it reveals any class imbalances and tells us which classes the model tend to confuse. An accuracy score of 90% may, for instance, seem very high. Without the context though, this is impossible to judge.