What's a good F1-score in (not) extremely imbalanced dataset? I have a dataset with around 4.7K focused on binary classification. Class proportion is 33:67. meaning Label 1 is 1558 (33%) and Label 0 is 3154 (67%) of my dataset.

Is my dataset imbalanced? some people say it is not bad

My objective is to increase the F1-score only. I set the class_weight=balanced in my parameters and scoring=f1 during CV as shown below.

svm=SVC(random_state=42)
svm_cv=GridSearchCV(svm,param_grid,cv=5,scoring='f1')
svm_cv.fit(X_train_std,y_train)


Can you let me know through a code sample on how I can increase the weightage to minority class? if that is any different from choosing balanced paramter

Currently my results are like as follows



I understand AUC for few algo is above 80 but I believe F1-score is more important for a imbalanced class problems like mine.

Can you help? I tried Oversampling minority class but not much improvement.

Increasing features doesn't take me to 80% F1-score
I would say you data in not imbalanced. 33:67 is not a bad ratio but Try using under sampling of majority class. 
As another option you can try difference algorithms like randomforest. You can also try boosting.