Author's): Shahidullah Kawsar
Originally published in Towards Artificial Intelligence.
Machine Learning Interview Preparation, Part 16
A confusion matrix is a table used to evaluate the performance of a classification model by comparing the predicted labels with the actual labels. It summarizes the results into four key outcomes: true positives, true negatives, false positives and false negatives. This structure shows not only how many predictions were correct, but also the types of errors the model made. From the confusion matrix, important metrics such as accuracy, precision, recall, and F1 score can be calculated. This is particularly useful in unbalanced datasets where accuracy alone can be misleading.
In this article, we delve into the concept of a confusion matrix, detailing its key elements and how it serves as a key tool in assessing the performance of a classification model, especially under class imbalance conditions. It highlights the importance of calculating metrics such as accuracy, precision, recall, and F1 score from the confusion matrix. The author also asks questions to test the reader's understanding of these concepts, encouraging engagement through hands-on scenarios and thoughtful analysis of methods for assessing model performance.
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Published via Towards AI
