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我正在尝试为 Mercedes Greener Manufacturing 数据集建立一个通用模型。所以，我正在尝试使用 XGBoost Regressor 来达到同样的效果。我使用了一个 1-100 的循环作为训练测试集的种子，以便获得更好的采样。我使用 PCA 将尺寸减小到 8 。

如何使用 xgboost 进行微调，以免我得到过拟合的模型？

X_train,X_test,y_train,y_test = train_test_split(X_pc,
                                                     y,
                                                     test_size=0.2,
                                                   random_state = i)
    model = XGBRegressor()
    model.fit(X_train,y_train)
    
    train=model.score(X_train,y_train)
    test=model.score(X_test,y_test)

输出

TEST: 0.28278595203767265 TRAIN: 0.9041892366322192 RS: 0
TEST: 0.3803514386218507 TRAIN: 0.9099759411069458 RS: 1
TEST: 0.3357132066270113 TRAIN: 0.9113739827130357 RS: 2
TEST: 0.3003256802391573 TRAIN: 0.901560899846001 RS: 3
TEST: 0.3769044561739856 TRAIN: 0.9034886060173257 RS: 4
TEST: 0.3449160536081909 TRAIN: 0.9092295020552124 RS: 5
TEST: 0.43083817087609166 TRAIN: 0.8957931397175393 RS: 6
TEST: 0.27375366705147564 TRAIN: 0.912349291318306 RS: 7
TEST: 0.39315883169376264 TRAIN: 0.9090768492254802 RS: 8
TEST: 0.38714220182913905 TRAIN: 0.9089864030990132 RS: 9
TEST: 0.37089065589124093 TRAIN: 0.9099379400411342 RS: 10
TEST: 0.3785854487827084 TRAIN: 0.9080405667805768 RS: 11
TEST: 0.29249852154319345 TRAIN: 0.9057747080596891 RS: 12
TEST: 0.34881642748048425 TRAIN: 0.9077565004654295 RS: 13
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1 回答 1

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争论是为了确保拆分的random_state可重复性，以便运行您的实验的其他人可以重新创建您的结果。

有许多方法可以有效地训练模型并减少过度拟合的机会。一种这样的策略是使用交叉折叠验证和网格搜索来确定模型的最佳参数。这是您的模型的外观。

import xgboost as xgb
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold


X_train,X_test,y_train,y_test = train_test_split(X_pc, y, test_size=0.2)
xgb_model = xgb.XGBRegressor()

# Define the parameter options for the Grid Search
paramters = {}

# Define the GridSearch class, including cross-validation, using your XGBoost model
clf = GridSearchCV(xgb_model, parameters, cv=StratifiedKFold(n_folds=5, shuffle=True).split(X_train, y_train))

# Fit the model
clf.fit(X_train, y_train)

要检索得到的最佳估计器，例如在训练期间表现最佳的 XGBoost 版本，您可以执行以下操作：

best_estimator = clf.best_estimator_
于 2020-12-18T05:01:16.097 回答