# Precision and recall of imbalanced classes

The point is the area under PR curve of my binary classes is the same as the version I took SMOTE.

Here is my pseudo code:

X_train, y_train, X_test, y_test = split(data)
smote_X_train, smote_y_train = SMOTE(X_train, y_train)
myclassifier.fit(smote_X_train, smote_y_train)
myclassifire.predit(X_test)


And here is the result that I've got:

Yellow - SMOTE

Blue - Imbalanced classes

Should yellow increase, right? This confuses me.

I guess the possible causes of this is either an algorithm or features. Am I right?