Confusion Matrix The confusion matrix is a performance evaluation tool used in classification problems to measure the accuracy of a classification model by comparing predicted and actual class labels. It provides a tabular summary of how well a mod… Continue reading

Confusion Matrix
The confusion matrix is a performance evaluation tool used in classification problems to measure the accuracy of a classification model by comparing predicted and actual class labels. It provides a tabular summary of how well a model’s predictions align with the actual outcomes. The confusion matrix summarizes how successful the model is at predicting the different classes of a data set sample, with each axis representing the predicted and actual labels. This metric lets us easily compare how many examples from a specific class were correctly predicted or mislabeled.
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