I have an xgboost classifier model that is trained to predict a binary target (0, 1). I followed the steps outlined in the notebooks, but the converted xgboost model predicts values on a continuous distribution between -6 and 10. The original base xgboost model does give prediction outputs of 0 and 1 only. Is there a decision boundary enforced by the volta model by which I could post-process the output, or does Volta not yet support classification? Thank you!
I have an xgboost classifier model that is trained to predict a binary target (0, 1). I followed the steps outlined in the notebooks, but the converted xgboost model predicts values on a continuous distribution between -6 and 10. The original base xgboost model does give prediction outputs of 0 and 1 only. Is there a decision boundary enforced by the volta model by which I could post-process the output, or does Volta not yet support classification? Thank you!