Wensi-Tang
integration with XGBoost
Hi Wensi! Hope all is well
Still working on this btc problem. Question is regarding XGBoost. Apparently boosting allows one to take a group of models which perform slightly better than a coinflip (weak learners), and ensemble them into a single, more powerful model (strong learner).
Do you have any ideas on how one would integrating the XGBoost framework into OSCNN for binary classification? We are able to produce a series of weak learners, and now we would like them to boost each other into a more predictive model.
Any thoughts, tips, ideas, or work you've done on this would be most appreciated -- thank you much
Hi, I am sorry, I only heard the term "Boosting" from my supervisor, and I have no experience with it. So, all I could do is just answer some specific questions :(
