我想知道我当前的程序是否正确，或者我是否可能存在数据泄漏。在导入数据集后，我按80/20的比例进行了分割。
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0, stratify=y)然后，在定义了CatBoostClassifier之后，我对训练集进行了带有交叉验证的网格搜索。
clf = CatBoostClassifier(leaf_estimation_iterations=1, border_count=254, scale_pos_weight=1.67)grid = {'learning_rate': [0.001, 0.003, 0.006,0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 0.9], 'depth': [1, 2,3,4,5, 6,7,8,9, 10], 'l2_leaf_reg': [1, 3, 5, 7, 9,11,13,15], 'iterations': [50,150,250,350,450,600, 800,1000]}clf.grid_search(grid, X=X_train, y=y_train, cv=10)现在我想评估我的模型。我可以使用整个数据集进行k折交叉验证来评估模型吗？（如以下代码所示）
kf = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=0)scoring = ['accuracy', 'f1', 'roc_auc', 'recall', 'precision']scores = cross_validate( clf, X, y, scoring=scoring, cv=kf, return_train_score=True)print("Accuracy TEST: %0.2f (+/- %0.2f) Accuracy TRAIN: %0.2f (+/- %0.2f)" % (scores['test_accuracy'].mean(), scores['test_accuracy'].std() * 2, scores['train_accuracy'].mean(), scores['train_accuracy'].std() * 2))print("F1 TEST: %0.2f (+/- %0.2f) F1 TRAIN : %0.2f (+/- %0.2f) " % (scores['test_f1'].mean(), scores['test_f1'].std() * 2, scores['train_f1'].mean(), scores['train_f1'].std() * 2))print("AUROC TEST: %0.2f (+/- %0.2f) AUROC TRAIN : %0.2f (+/- %0.2f)" % (scores['test_roc_auc'].mean(), scores['test_roc_auc'].std() * 2, scores['train_roc_auc'].mean(), scores['train_roc_auc'].std() * 2))print("recall TEST: %0.2f (+/- %0.2f) recall TRAIN: %0.2f (+/- %0.2f)" % (scores['test_recall'].mean(), scores['test_recall'].std() * 2, scores['train_recall'].mean(), scores['train_recall'].std() * 2))print("Precision TEST: %0.2f (+/- %0.2f) Precision TRAIN: %0.2f (+/- %0.2f)" % (scores['test_precision'].mean(), scores['test_precision'].std() * 2, scores['train_precision'].mean(), scores['train_precision'].std() * 2))还是我应该只在训练集上进行k折交叉验证？
回答：
你通常会将交叉验证作为训练过程的一部分。它旨在找到模型的良好参数。只有在最后，你才应该在测试集上评估你的模型——这些数据在之前的模型训练和交叉验证过程中都没有被使用过。这样你就不会泄露任何数据。
所以，是的，你应该只在训练集上进行交叉验证。并只使用测试集进行最终评估。
