XGBoost API 参数 分类问题 使用逻辑回归 # Import xgboost import xgboost as xgb # Create arrays for the features and the target: X, y X, y = churn_data.iloc[:,:-1], churn_data.iloc[:,-1] # Create the training and test sets X_train, X_test, y_train, y_test= train_t
xgboost入门非常经典的材料,虽然读起来比较吃力,但是会有很大的帮助: 英文原文链接:https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/ 原文地址:Complete Guide to Parameter Tuning in XGBoost (with codes in Python) 译注:文内提供的代码和运行结果有一定差异,可以从这里下
欢迎关注博主主页,学习python视频资源 https://blog.csdn.net/q383700092/article/details/53763328 调参后结果非常理想 from sklearn.model_selection import GridSearchCV from sklearn.datasets import load_breast_cancer from xgboost import XGBClassifier from sklearn.model_selection