我似乎无法让XGBoost连续两次给出相同的结果。在sklearn中，我似乎可以使用random_state，但这在XGBoost中不起作用。
我还尝试设置了seed、subsample、colsample_bytree（将subsample和colsample_bytree设置为1似乎没有影响）。
有什么建议可以让我重现结果，有点像设置sklearn中的random_state值吗？
为了完整起见，这里提供了一些代码，但我认为你可能更需要关注我问题底部的模型部分。
预处理
from sklearn.impute import SimpleImputerfrom sklearn.preprocessing import LabelEncoder#数值列numerical_columns_list = [colname for colname in X_train.columns if X_train[colname].dtypes in ['int64', 'float64']]X_train_trf = X_train.copy()X_valid_trf = X_valid.copy()# 对数值数据进行预处理num_imputer = SimpleImputer(strategy='median')X_train_trf[numerical_columns_list] = num_imputer.fit_transform(X_train_trf[numerical_columns_list])X_valid_trf[numerical_columns_list] = num_imputer.transform(X_valid_trf[numerical_columns_list])# 对分类数据进行预处理categorical_columns_list = [colname for colname in X_train.columns if X_train[colname].dtypes == 'object' ]cat_imputer = SimpleImputer(strategy='most_frequent')X_train_trf[categorical_columns_list] = cat_imputer.fit_transform(X_train_trf[categorical_columns_list])X_valid_trf[categorical_columns_list] = cat_imputer.transform(X_valid_trf[categorical_columns_list])le = LabelEncoder()for col in X_train_trf[categorical_columns_list].columns: X_train_trf[col] = le.fit_transform(X_train_trf[col]) X_valid_trf[col] = le.fit_transform(X_valid_trf[col])模型
from xgboost import XGBRegressorfrom sklearn.metrics import mean_absolute_errormodel = XGBRegressor(n_estimators=1000, learning_rate=0.05, subsample=0.8, colsample_bytree= 0.8, seed=42)model.fit(X_train_trf,y_train, early_stopping_rounds=5, eval_set=[(X_train_trf, y_train), (X_valid_trf, y_valid)], verbose=False)preds = model.predict(X_valid_trf)回答：
作为参考，问题不在于XGBoost，而在于数据分割。感谢Venkatachalam指出这一点！
我用train_test_split分割数据时没有设置random_state！
修正如下：
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size = 0.2, random_state=1)