There are a few differences between using the XGBoost library's own implementation of cross-validation (i.e.,
xgboost.cv()) and using the cross-validation functions provided by scikit-learn (i.e.,
One difference is that
xgboost.cv() only works with XGBoost models, whereas the scikit-learn functions can be used with a variety of model types. This means that if you want to use XGBoost with scikit-learn's cross-validation functions, you will need to use XGBoost's scikit-learn API, which wraps the XGBoost library's functionality in a way that is compatible with scikit-learn.
Another difference is that
xgboost.cv() is optimized specifically for XGBoost models, whereas the scikit-learn functions are more general and can be used with any estimator. This means that
xgboost.cv() may be faster or more efficient when working with XGBoost models, but it may not be as flexible as the scikit-learn functions.
As for the use of
DMatrix is a data structure provided by the XGBoost library that is optimized for use with XGBoost models. It can be more efficient to use
DMatrix when working with large datasets, since it stores the data in a more compact format and can take advantage of XGBoost's internal memory management. However, it is also possible to use
pandas.DataFrame with XGBoost models by converting the DataFrame to a
DMatrix using the
In short, if you need the flexibility and compatibility of scikit-learn's functions, or if you are using a model other than XGBoost, you may want to use the scikit-learn functions. On the other hand, if you are only using XGBoost models and want the efficiency and optimization provided by the XGBoost library, you may prefer to use