Hi, all

Based on xgboost documentation, min_child_weight is treated as number of observation for rmse objective and is usually chosen as int such as https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor.

However, I recently read a article saying that "With XGBoost, you can specify this parameter as a float as well. If you do, it will use a percentage of samples to determine leaf nods." (https://kevinvecmanis.io/machine%20learning/hyperparameter%20tuning/dataviz/python/2019/05/11/XGBoost-Tuning-Visual-Guide.html#child_weight). I cannot find a second source using min_child_weight as a float.

So I am confused and worried that is it true that we could set min_child_weight as a float?? Any comfirmation would be appreciated.