Sep 16, 2016 · Is it possible to train a model by xgboost that has multiple continuous outputs (multi-regression)? What would be the objective of training such a model?

Jun 4, 2016 · 19 According to this post there 3 different ways to get feature importance from Xgboost: use built-in feature importance, use permutation based importance, use shap based …

Mar 28, 2017 · i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and …

Mar 9, 2025 · I would like to create a custom loss function for the "reg:pseudohubererror" objective in XGBoost. However, I am noticing a discrepancy between the results produced by …

Jan 3, 2018 · The sample_weight parameter allows you to specify a different weight for each training example. The scale_pos_weight parameter lets you provide a weight for an entire …

Dec 14, 2015 · "When using XGBoost we need to convert categorical variables into numeric." Not always, no. If booster=='gbtree' (the default), then XGBoost can handle categorical variables …

I wonder if xgboost also uses this approach using information gain or accuracy as stated in the citation above. I've tried to dig in the code of xgboost and found out this method (already cut …

ImportError: No module named 'xgboost.xgbclassifier', I tried using your command, it returned this.

Apr 10, 2019 · I have a question about xgboost classifier with sklearn API. It seems it has a parameter to tell how much probability should be returned as True, but i can't find it. Normally, …

Jun 25, 2018 · The idea of "subsample", "colsample_by_tree", and "colsample_bylevel" comes from Random Forests. In it, you build an ensemble of many trees and then group them …