xgboost, CALCE

 

 

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.

[링크 : https://github.com/dmlc/xgboost]

 

[링크 : https://monawa.tistory.com/28]

[링크 : https://xgboost.readthedocs.io/en/stable/tutorials/param_tuning.html]

[링크 : https://www.kaggle.com/code/lifesailor/xgboost]

 

 

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[링크 : https://calce.umd.edu/battery-data]

 

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grid search cv

[링크 : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html]

[링크 : https://day-to-day.tistory.com/33]

[링크 : https://velog.io/@hyunicecream/GridSearchCV란-어떻게-사용할까]

[링크 : https://wikidocs.net/87220]