University of Michigan - Ann Arbor

The confusion matrix is a performance measurement method that can be used for binary classification and multi-class classifications.  It is usually shown shown in a table format with the four possible outcomes: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).  

Confusion Matrix

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

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Machine Learning Yearning (Deeplearning.ai)

Chapter 10: Evaluating Model Performance
Section: A closer look at confusion matrices

Lantz, Brett. Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems (p. 317-318). Kindle Edition. 


How to read confusion matrix for binary classification

- True Positive (TP)
- True Negative (TN)
- False Positive (FP)
- False Negative (FN)

Components of Confusion Matrix

The multi-class confusion matrix is a straightforward extension of the binary classifier two by two confusion matrix.
For example, for classification of hand-written digits, there are ten classes for each digit, zero through nine. So, the ten-class confusion matrix is a 10x10 matrix with the true digit class indexed by row and the predicted digit class indexed by column. Like the 2x2 case, the correct prediction is by the classifier where the true class matches the predicted class are all along the diagonal and misclassifications are off the diagonal.

In this example, most of the predictions are correct with only a few misclassifications. The most frequent type of mistake here is misclassifying the true digit, 8 as a predicted digit 1 which happened three times.
And the overall accuracy is high, about 97%.

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