It may be, that the test set is so highly imbalanced that simply predicting the majority class yields such an accuracy. Without the context though, this is impossible to judge. ![]() 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.Īn accuracy score of 90% may, for instance, seem very high. If we have two classes (0, 1), we have these 4 possible combinations of predictions and targets: Targetįor each combination, we can count how many times the model made that prediction for an observation with that target. When inspecting a classification model’s performance, a confusion matrix tells you the distribution of the predictions and targets.
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