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?

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 …

Jul 1, 2017 · import sys !{sys.executable} -m pip install xgboost This allows the package to be installed with right on Jupyter notebook.

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 …

Jun 7, 2021 · sample_weight parameter is useful for handling imbalanced data while using XGBoost for training the data. You can compute sample weights by using …

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

Mar 4, 2025 · XG Boost Categorical Issues during Time Series Prediction I observed an issue where categorical variables are being converted back to strings, causing failures. The issue …

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 …

Dec 17, 2025 · There is an existing xgboost model in the pipeline that was created using this container sagemaker.image_uris.retrieve('xgboost', sagemaker.Session().boto_region_name, …

Nov 17, 2015 · File "xgboost/libpath.py", line 44, in find_lib_path 'List of candidates:\n' + ('\n'.join(dll_path))) __builtin__.XGBoostLibraryNotFound: Cannot find XGBoost Libarary in the …