binary classification for imbalanced data
Also you can use asymmetric loss functions which penalize models differently based on the label of data. In your case the loss function should penalize errors in "bad" samples much more than errors in "good" samples. In this way the model pays more attentions to the rare data points.
In data mining, I use a machine learning algorithm to solve the binary classification.
However, the distribution of data samples is extremely imbalanced.
The ratio between good samples and bad samples is as high as 500:1.
Which methods can be used to solve the binary classification for imbalanced data?