A confusion matrix is a tool used to evaluate the performance of a classification model by comparing predicted and actual class labels. It summarizes the model’s predictions into four key categories:
From this matrix, we can derive important evaluation metrics like accuracy, precision, recall, and F1-score, which provide deeper insight into model performance beyond overall accuracy.
Accuracy alone can be misleading, especially in imbalanced datasets, where one class is much more frequent than the other. The confusion matrix allows for a more nuanced assessment, helping to determine whether the model is biased towards a particular class. Precision and recall are particularly useful when false positives or false negatives have different real-world consequences, such as in medical diagnosis or fraud detection.
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