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
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