Judging from what you said, that means you used SMOTE to balance the classes, SMOTE OVERSAMPLING should be deone before training the model and only on the train dataset
Yes man smote don on training data set only but result not good as i metioned before ,i thing bbad result due to train data need to increased , is this bad reasult isnormal and more than one classes predicted
It's because of the imbalance of the classes some classes have just 2 instances,n depends on ur cross val method too
Man ,data already balanced befor modilng ,do you mean that data balncing sholud be done inside model with cross val for each model will be used ?
Judging from what you said, that means you used SMOTE to balance the classes, SMOTE OVERSAMPLING should be deone before training the model and only on the train dataset
Yes man smote don on training data set only but result not good as i metioned before ,i thing bbad result due to train data need to increased , is this bad reasult isnormal and more than one classes predicted
Try changing your evaluation metric
Thanks man