multioutput regression by xgboost - Stack Overflow
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?
XGBoost Categorical Variables: Dummification vs encoding
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 …
ImportError: No module named xgboost - Stack Overflow
ImportError: No module named 'xgboost.xgbclassifier', I tried using your command, it returned this.
How to get feature importance in xgboost? - Stack Overflow
Jun 4, 2016 · How to get feature importance in xgboost? Asked 9 years, 6 months ago Modified 4 years ago Viewed 249k times
How to get the list of features which are actually used by XGBoost
Apr 17, 2023 · The correct approach would be to traverse XGBoost tree data structure, and collect node split indices (which correspond to column indices in your training dataset). If your …
python - XGBoost GPU version not outperforming CPU on small …
May 2, 2025 · I'm currently working on a parallel and distributed computing project where I'm comparing the performance of XGBoost running on CPU vs GPU. The goal is to demonstrate …
Custom loss function in XGBoost - Stack Overflow
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 …
Cannot import xgboost in Jupyter notebook - Stack Overflow
Jul 1, 2017 · import sys !{sys.executable} -m pip install xgboost This allows the package to be installed with right on Jupyter notebook.
Shap summary plots for XGBoost with categorical data inputs
Mar 31, 2023 · XGBoost supports inputting features as categories directly, which is very useful when there are a lot of categorical variables. This doesn't seem to be compatible with Shap: …
How can I install XGBoost package in python on Windows
XGBoost is used in Applied Machine Learning and is known for its gradient boost algorithm and it is available as a library in python but has to be compiled using cmake. Alternatively what you …