What is the rationale behind ROC curves?I am not sure how ROC curves work. I see that the X-Axis is the false positive rate while the Y axis is the true positive rate.1) I don't understand how for a given statistical learning model, you could have the true positive and false positive rate to vary from 0 to 1. Are you changing parameters in the model to make it so?2) What about true negatives and false negatives? How are these represented in the curve?

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What is the rationale behind ROC curves?I am not sure how ROC curves work. I see that the X-Axis is the false positive rate while the Y axis is the true positive rate.1) I don&#039;t understand how for a given statistical learning model, you could have the true positive and false positive rate to vary from 0 to 1. Are you changing parameters in the model to make it so?2) What about true negatives and false negatives? How are these represented in the curve?

lutzantsca885 · Accepted Answer

1) Yes, you are supposed to compare &quot;different models&quot; and the curve will help you identify which one is the best for your desired application. So, for instance, if you would like to know how well your system detects &quot;  X&quot;, you should vary a certain threshold value to see how well the system detects   X given the different conditions.2) The True Positive Rate is defined as:   T  P  R  =            T      P              T      P      +      F      N      , the True Negative Rate is defined as:   T  N  R  =            T      N              T      N      +      F      P      , where   T  P  = True Positives,   F  N  = False Negatives,   T  N  = True Negatives, and   F  P  = False Positives.As you can see, both of them consider the true negatives and the false negatives.

What is the rationale behind ROC curves? I am not sure how ROC curves work. I see that the X-Axis i

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