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?