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) …

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 the default …

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 arises during …

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 …

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 …