What is true about the confusion matrix below?

I The first entry, marked with the number 3784, represents the number of examples in the "No Default" class that the model predicted correctly.
II Outside the diagonal, there is the number of examples that the model predicted correctly.
III On the diagonal, there is the number of examples that the model predicted correctly.
IV This confusion matrix is related to a four-class classification problem, therefore is a 2-by-2 matrix.

Question

What is true about the confusion matrix below?

I	The first entry, marked with the number 3784, represents the number of examples in the "No Default" class that the model predicted correctly.
II	Outside the diagonal, there is the number of examples that the model predicted correctly.
III	On the diagonal, there is the number of examples that the model predicted correctly.
IV	This confusion matrix is related to a four-class classification problem, therefore is a 2-by-2 matrix.

BlackTom AI · Accepted Answer

To tackle this question, I will examine what each statement asserts about the given confusion matrix.
Option II and IV both require us to assess specific structural aspects of a confusion matrix.
Option II claims that outside the diagonal there is the number of examples the model predicted correctly. In reality, the diagonal entries are the correct predictions (the true positives for each class), while the off-diagonal entries are the misclassifications. Therefore, outside the diagonal should not be described as correct predictions, making this statement inaccurate.
Option IV states that the matrix is related to a four-class classification problem, therefore it is a 2-by-2 matrix. A four-class problem would produce a 4-by-4 matrix, not 2-by-2. This makes the statement false.
Now turn to Option I: It says the first entry, marked with 3784, represents the number of examples in the "No Default" class that the model predicted correctly. In a typical confusion matrix layout, the top-left entry corresponds to actual No Default and predicted No Default, i.e., correct predictions for the No Default class. This description matches the conventional interpretation, so this statement is plausible.
Option III asserts that on the diagonal, there is the number of examples that the model predicted correctly. This is exactly how diagonals in a confusion matrix are defined: each diagonal cell is a correct prediction for its corresponding class. Hence this statement is also correct.
Given these analyses, Option II is incorrect because it mischaracterizes correct predictions as off-diagonal data, and Option IV is incorrect due to the miscount of matrix dimensions for a four-class problem. Options I and III accurately describe the entries on the confusion matrix according to standard definitions, whereas the other statements contradict the standard interpretation of confusion matrices.

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