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 · 20 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 importance. …

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 …

Jan 11, 2024 · I am trying to convert XGBoost shapely values into an SHAP explainer object. Using the example [here] [1] with the built in SHAP library takes days to run (even on a subsampled dataset) …

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

Apr 7, 2020 · I am probably looking right over it in the documentation, but I wanted to know if there is a way with XGBoost to generate both the prediction and probability for the results? In my case, I am …

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 …

Nov 14, 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, version='latest')

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 compute_sample_weight() of sklearn …

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 off irrelevant …