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The confusion matrix tells us how often a sample that should have been x was classified as something different and it tells us what it was classified as.
From this it is easy to see if the classifier confuses two classes.
This can be used to improve the accuracy of the system by introducing new features to the classifier responsible for seperating specific classes.