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Hi! Love the package, but one issue - `shap_values` is implemented inconsistently across the package documentation.
For example, when plotting a summary plot:
`explainer = shap.TreeExplainer(mo…
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Any plans to adjust `SparkXGBRegressor` and `SparkXGBClassifier` to support custom https://xgboost.readthedocs.io/en/stable/tutorials/custom_metric_obj.html as per the standard python implementation?
…
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https://tdhock.github.io/change-tutorial/Supervised.html#learning-and-prediction
![Untitled](https://github.com/lamtung16/ML_ChangepointDetection/assets/932850/c36642e6-c6e5-465d-bfa0-c764ae1ba246)…
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There are large differences between SHAP values calculated using CPU and GPU for XGBoost models with `feature_perturbation='interventional'` and `model_output='log_loss'`. The detailed description of …
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Hi,
It would be great if this can be added. As far as I can see, it would require adding the following to 'build_fit_formula_xgb'
```
else if (objective %in% c("count:poisson")) {
assign…
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I've created a Sagemaker XGBOOST model and compiled it for NEO using:
```
compiled_model = xgb.compile_model(target_instance_family='rasp3b',
role=role,
…
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### Project URL
https://pypi.org/project/xgboost
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PyPI
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https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb
I can't find documentation for "_Booster" met…
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The conda environment uses very outdated versions of several packages, most notably XGBoost, Uproot and Awkward Arrays. There have been substantial improvements and interface changes in many of these …
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Hello everyone. I'm trying to write my own `keep_best_model_instance` function, by saving my Booster when the validation loss decreases. I understand there is a specific function to predict on the bes…