数据挖掘 - 为什么 GridSearchCV 返回 nan？ - 吾爱随笔录

我正在使用 gridsearchcv 来调整我的模型的参数，并且我还使用管道和交叉验证。当我运行模型来调整 XGBoost 的参数时，它返回nan. 但是，当我对随机森林等其他分类器使用相同的代码时，它可以工作并返回完整的结果。

kf = StratifiedKFold(n_splits=10, shuffle=False)

SCORING = ['accuracy', 'precision', 'recall', 'f1' ]

# define parametres for hypertuning
params = {
    'Classifier__n_estimators': [5, 10, 20, 50, 100, 200]
}

XGB = XGBClassifier()
UnSam = RepeatedEditedNearestNeighbours()

pipe = Pipeline(steps=[('UnderSampling', UnSam ), ('Classifier', XGB)])
# ___________________________________________

mod = GridSearchCV(pipe, params, cv =kf, scoring = SCORING, refit='f1', return_train_score=True)
mod.fit(X_train, y_train)

这是我的代码，当我运行它时，得到以下结果：

{'Classifier__n_estimators': 5}
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F1 :  [nan nan nan nan nan nan] 
 Recall :  [nan nan nan nan nan nan] 
 Accuracy :  [nan nan nan nan nan nan] 
 Precision :  [nan nan nan nan nan nan]

另一件奇怪的事情是，当我在 Logistics Regression 中应用相同的代码来调整惩罚时，它会为 l1 和 elasticnet 返回 nan。

kf = StratifiedKFold(n_splits=10, shuffle=False)

SCORING = ['accuracy', 'precision', 'recall', 'f1' ]

# define parametres for hypertuning
params = {
    'Classifier__penalty': ['l1','l2','elasticnet']
}

LR = LogisticRegression(random_state=0)
UnSam = RepeatedEditedNearestNeighbours()

pipe = Pipeline(steps=[('UnderSampling', UnSam ), ('Classifier', LR)])
# ___________________________________________

mod = GridSearchCV(pipe, params, cv =kf, scoring = SCORING, refit='f1', return_train_score=True)
mod.fit(X_train, y_train)

结果如下：

{'Classifier__penalty': 'l2'}
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F1 :  [  nan 0.363   nan] 
 Recall :  [   nan 0.4188    nan] 
 Accuracy :  [   nan 0.7809    nan] 
 Precision :  [   nan 0.3215    nan]