GridSearchCV 给出的结果与我的手动调整程序不同

如何解决GridSearchCV 给出的结果与我的手动调整程序不同

当我使用 sklearn 和手动进行 GridSearch 时，我得到不同的结果。

第一个代码块是我使用 sklearn 运行 GridSearch 时的程序：

from sklearn import ensemble
from sklearn import metrics
from sklearn import model_selection
from sklearn import pipeline
from imblearn.pipeline import Pipeline
from imblearn.under_sampling import RandomUnderSampler


X = folded_train.drop(columns = ["10_fold","class_encoded"])
y = folded_train["class_encoded"]
ten_fold = folded_train["10_fold"]


logo = LeaveOneGroupOut()
cross_val_groups = logo.split(X,y,ten_fold)


classifier = (Pipeline([("sampling",RandomUnderSampler()),("classifier",ensemble.RandomForestClassifier(n_jobs = -1))]))

param_grid = {
    "classifier__n_estimators" : [100,200,300,400,600],"classifier__max_depth": [1,3,5,7],"classifier__criterion": ["gini","entropy"]
}

model = model_selection.gridsearchcv(
    estimator = classifier,param_grid = param_grid,scoring = "roc_auc",verbose = 10,n_jobs = 1,cv = cross_val_groups
)

model.fit(X,y)

我正在尝试手动执行相同的程序。这是我的代码：

from sklearn import ensemble
from sklearn import metrics
from sklearn import model_selection
from sklearn import pipeline
from imblearn.pipeline import Pipeline
from imblearn.under_sampling import RandomUnderSampler


X = folded_train.drop(columns = ["10_fold","class_encoded"])
y = folded_train["class_encoded"]
ten_fold = folded_train["10_fold"]



number_of_estimators = [100,300]
maximum_depths =  [1,7]
criterions = ["gini","entropy"]

logo = LeaveOneGroupOut()


for criterion in criterions:
    for max_depth in maximum_depths:
        for n_of_estimator in number_of_estimators:
            
            for train_index,val_index in logo.split(X,ten_fold):
                aPipeline = (Pipeline(steps=[('sampling',('classifier',ensemble.RandomForestClassifier(criterion= criterion,max_depth= max_depth,n_estimators= n_of_estimator,n_jobs=-1))]))
                X_trn,X_vl = X.iloc[train_index],X.iloc[val_index]
                y_trn,y_vl = y.iloc[train_index],y.iloc[val_index]

                aPipeline1.fit(X_trn,y_trn)

                predictions = aPipeline1.predict(X_vl)

                print("Criterion",criterion," Max Depth",max_depth,"Number of estimator ",n_of_estimator,"score ",metrics.roc_auc_score(y_vl,predictions))

对于 sklearn gridsearchcv，我获得了以下特定参数的分数 (roc_auc)：

对于标准 = "gini"，max_depth = 1 和 n_estimators = 100， [0.786,0.799,0.789,0.796,0.775,0.776,0.779,0.788,0.770,0.769] 每次 cv 迭代

我得到的相同参数的手动执行： [0.730,0.749,0.714,0.710,0.732,0.724,0.711,0.715,0.734]

这个结果也适用于其他参数组合。导致这种情况的因素有哪些？

注意：我找到了这个，但这不是我问题的答案：Why GridSearchCV model results are different than the model I manually tuned?