我创建了这个演示来演示从库内部抛出的错误。此代码将数据集拆分为训练/评估/测试，并使用训练/评估进行超参数搜索、提前停止，而测试集则保留以供稍后评估。我缩小了与 GridSearchCV 的交叉验证相关的错误，但我无法找出确切的根本原因并进行修复。
from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import xgboost as xgb
iris = datasets.load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target'])
X, y = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']], df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train_split, X_eval_split, y_train_split, y_eval_split = train_test_split(X_train, y_train, test_size=0.25, random_state=42)
parameters = {
'max_depth': (2, 3, 4),
} …