Here is an implementation corresponding to Tom Fawcett's algorithm 3 in "roc graphs: notes and practical considerations for researchers, " 2004.

function auc=areaundercurve(FPR,TPR);

% given true positive rate and false positive rate calculates the area under the curve

% true positive are on the y-axis and false positives on the x-axis

% sum rectangular area between all points

% example: auc=areaundercurve(FPR,TPR);

[x2,inds]=sort(FPR);

x2=[x2,1]; % the trick is in inventing a last point 1,1

y2=TPR(inds);

y2=[y2,1];

xdiff=diff(x2);

xdiff=[x2(1),xdiff];

auc1=sum(y2.*xdiff); % upper point area

auc2=sum([0,y2([1:end-1])].*xdiff); % lower point area

auc=mean([auc1,auc2]);

function [TP,FP]=getfptp(T,Y)

Y(Y>=0)=1;Y(Y<0)=-1; % target class (positive) is 1

TP=sum( ( (Y==1) + (T==1) )==2 );

FN=sum( ( (Y==-1) + (T==1) )==2 );

FP=sum( ( (Y==1) + (T==-1) )==2 );

TN=sum( ( (Y==-1) + (T==-1) )==2 );

TP=TP/(TP+FN);

FP=FP/(FP+TN);

end

## 2 comments:

Very interesting blog post.Quite informative and very helpful.This indeed is one of the recommended blog for learners.Thank you for providing such nice piece of article. I'm glad to leave a comment. Expect more articles in future. You too can check this R Programming tutorial for updated knowledge on R Programming.

https://www.youtube.com/watch?v=rgFVq_Q6VF0

What a fantastic read on Data Science. This has helped me understand a lot in Data Science course. Please keep sharing similar write ups on Data Science. Guys if you are keen to know more on Data Science, must check this wonderful Data Science tutorial and i'm sure you will enjoy learning on Data Science training.:-https://www.youtube.com/watch?v=gXb9ZKwx29U&t=237s

Post a Comment