How to get feature importance in xgboost Asked 9 years, 6 months ago modified 4 years ago viewed 249k times When using xgboost we need to convert categorical variables into numeric. not always, no Whereas if the label is a string (not an integer) then yes we need to comvert it. No module named 'xgboost.xgbclassifier', i tried using your command, it returned this. 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) while the xgboost library takes a few minutes. 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 reg:pseudohubererror objective and my custom loss function. 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 tryin. File xgboost/libpath.py, line 44, in find_lib_path 'list of candidates:\n' + ('\n'.join(dll_path))) __builtin__.xgboostlibrarynotfound
Does anyone know how to install xgboost for python on windows10 platform During gridsearch i'd like it to early stop, since it reduce search time drastically and (expecting to) have
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