# Calculating a Confusion Matrix

Can someone help me understand how to find the values of a confusion matrix?

I know that essentially a confusion matrix looks like this:

True Positive |False Positive
False Negative|True Negative


I've encountered a problem where I have around 1000 cases in my test data.

In the approximate middle of its ROC chart there is a point where the false positive rate is 0.5, the true positive rate is 0.7, and the accuracy is 0.7.

This is my approach to this problem and I just want to verify if I am doing it correctly:

To calculate the count of True Positives:

$$0.7 * 1000\,Cases = 700\,Cases$$

To calculate the count of False Positives:

$$0.5 * 1000\,Cases = 500\,Cases$$

I would assume calculating the True Negatives would be $1000 - 700 = 300$.

Then solving for False Negatives =

$$.7 (Accuracy) = 700 + 300 / 700 + 300 + 500 + FN$$

Can someone confirm if this is the right approach?

• Of your 1000 test cases, how many are of the positive class and negative class respectively? – timleathart Oct 29 '17 at 23:53