README.md

In zhangyuqiangarchie/XGBparsel2: Automatic parameter adjustment method for xgb.cv

XGBparsel2

An automatic parameter tuning method for xgboost

About the xgb.auc function in xgbparsel package:

The cross validation function xgb.cv in xgboost package only performs cross validation and does not select relatively excellent parameters. In order to overcome this problem, xgb.auc is an automatic parameter adjustment function suitable for binary classification. The function creates a cycle with the number of N and the parameter max_ Depth is randomly selected from 6-10. Parameter ETA follows a uniform distribution of 0.01-0.3, parameter gamma follows a uniform distribution of 0-0.2, parameter subsample follows a uniform distribution of 0.6-0.9, and parameter colsample follows a uniform distribution_ Bytree obeys the uniform distribution of 0.5-0.8, parameter min_ child_ Weight randomly selected from 1-40, parameter Max_ delta_ Step is randomly selected from 1-10. The value range of all parameters is the typical parameter value in xgboost algorithm. Through cross validation, the parameters corresponding to the maximum AUC on the test set are selected, and the optimal parameters are obtained through continuous iteration.

The xgbparsel package also comes with the function xgb.rmse. The principle of this function is similar to xgb.auc. But the difference is that xgb.rmse is used for regression rather than classification.

zhangyuqiangarchie/XGBparsel2 documentation built on Dec. 23, 2021, 9:18 p.m.