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. …

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

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 …

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 arises during …

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 …

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) …

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 …

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 …

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 …