14
Apr
Calculating False Positive Rate. True positives for class B 25. False Positive Rate indicates the proportion of people classified as not having heart disease that are False Positives. Of 1 million with the virus 99 of them get correctly banned about 1 million. Now I need the f1-measure value.
False negatives for class B. 1 004 096. That a positive result will be given when the true value is negative. So if you get banned there is only a 9 chance you actually have the virus. To get to know the ROC better lets draw one from scratch. Confusion matrix structure for binary classification problems.
It quantifies the avoidance of false positive.
The probability a prospective employee tests positive when they did not in fact take drugs the false positive rate which is 5 or 005. Yet Weka tells me that the false positive rate for class B is 0070. The results provided in the above calculation are the following. The ROC curve is produced by calculating and plotting the true positive rate against the false positive rate for a single classifier at a variety of thresholdsFor example in logistic regression the threshold would be the predicted probability of an observation belonging to the positive class. So if you get banned there is only a 9 chance you actually have the virus. False negatives for class B.