CODEWITHSUNDEEP
pythonmachine-learningclassificationrocauc
Spedo
Spedo

Reputation: 365

Inverse ROC-AUC value?

I have a classification problem where I need to predict a class of (0,1) given a data. Basically I have a dataset with more than 300 features (including a target value for prediction) and more than 2000 rows (samples). I applied different classifiers as follows:

 1. DecisionTreeClassifier()
 2. RandomForestClassifier()
 3. GradientBoostingClassifier()
 4. KNeighborsClassifier()

Almost all the classifiers gave me similar results around 0.50 AUC value except Random forest around 0.28. I would like to know that whether it is correct if I inverse the RandomForest result like:

 1-0.28= 0.72

And report it as the AUC? Is it correct?

Upvotes: 0

Views: 4561

Answers (1)

desertnaut
desertnaut

Reputation: 60370

Your intuition is not wrong: if a binary classifier performs indeed worse than random (i.e. AUC < 0.5), a valid strategy is to simply invert its predictions, i.e. report a 0 whenever the classifier predicts a 1, and vice versa); from the relevant Wikipedia entry (emphasis added):

The diagonal divides the ROC space. Points above the diagonal represent good classification results (better than random); points below the line represent bad results (worse than random). Note that the output of a consistently bad predictor could simply be inverted to obtain a good predictor.

enter image description here

Nevertheless, the formally correct AUC for this inverted classifier, would be to first invert the individual probabilistic predictions prob of your model:

prob_invert = 1 - prob

and then calculate the AUC using these predictions prob_invert (arguably the process should give similar results with the naive approach you describe of simply subtracting the AUC from 1, but I'm not quire sure of the exact result - see also this Quora answer).

Needless to say, all this is based on the assumption that your whole process is correct, i.e. you don't have any modeling or coding errors (constructing a worse-than-random classifier is not exactly trivial).

Upvotes: 1

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