使用目标变量中不包含任何值的测试数据集进行训练-测试拆分

问：

我在 Python 中得到了这样的数据集（只是其中的一小部分，总共 20 个特征）：

分数值是分类的，它只能取 1、2、3、4 或 5 的值。

我预处理了数据，并使用 LabelEncoder 对分类特征（如状态）进行编码。

现在我想进行如下所示的训练-测试拆分：所有具有分数值的行都应位于训练集中，分数列中带有“NA”的所有行都应位于测试数据集中。

我使用 RandomForestClassifier 来查找我稍后将使用的 n 个最重要的功能。

之后我使用了 KNeighborsClassifier 和 RandomForestClassifier。

但是如果我进行交叉验证，这些模型的值非常低（大约 0.5），请参阅此处的代码：

    ### 5) Check models' performances 
    clf = RandomForestClassifier(max_depth=best_max_depth, random_state=42)
    clf.fit(X_train_subset, y_train)

    knn = KNeighborsClassifier(n_neighbors=best_k_value)
    knn.fit(X_train_subset, y_train)
    
    knn_test_score = knn.score(X_test_subset, y_test)
    clf_test_score = clf.score(X_test_subset, y_test)
    
    knn_train_score = knn.score(X_train_subset, y_train)
    clf_train_score = clf.score(X_train_subset, y_train)

    print(f"TEST data - kNN Score for {n_features_to_select} selected features: {knn_test_score:.3f}")
    print(f"TEST data - RF Score for {n_features_to_select} selected features: {clf_test_score:.3f}")

    print(f"TRAINING data - kNN Score for {n_features_to_select} selected features: {knn_train_score:.3f}")
    print(f"TRAINING data - RF Score for {n_features_to_select} selected features: {clf_train_score:.3f}")
    print("*" * 70)
     
    
    # Perform Cross Validation to avoid overfitting
    # Source: https://scikit-learn.org/stable/modules/cross_validation.html
    
    # Define the number of folds for cross-validation
    # Smaller values for n_folds mean larger validation sets ("test" sets out of the training data) and smaller training sets for each iteration -> more variability in the assessment
    # Higher values for n_folds mean samller validation sets ("test" sets out of the training data) and larger training sets for each iteration -> lower variability in the assessment
    # Recommended are values between 5 and 10
    # Source: https://machinelearningmastery.com/k-fold-cross-validation/
    n_folds = 10

    # Create a k-fold cross-validation iterator
    kf = KFold(n_splits=n_folds, shuffle=True, random_state=42)

    # Initialize models
    clf = RandomForestClassifier(max_depth=best_max_depth, random_state=42)
    knn = KNeighborsClassifier(n_neighbors=best_k_value)

    # Perform k-fold cross-validation for each model
    clf_train_scores = cross_val_score(clf, X_train_subset, y_train, cv=kf)
    knn_train_scores = cross_val_score(knn, X_train_subset, y_train, cv=kf)

    clf_test_scores = cross_val_score(clf, X_test_subset, y_test, cv=kf)
    knn_test_scores = cross_val_score(knn, X_test_subset, y_test, cv=kf)

    # Print the mean and standard deviation of the cross-validation scores
    print(f"Random Forest Classifier (RF) Cross-Validation Scores for {n_features_to_select} selected features:")
    print(f"Mean RF Score: {round(clf_train_scores.mean(), 3)}")
    print(f"Standard Deviation RF Score: {round(clf_train_scores.std(), 3)}")
    print("*" * 70)

    print(f"k-Nearest Neighbors (kNN) Cross-Validation Scores for {n_features_to_select} selected features:")
    print(f"Mean kNN Score: {round(knn_train_scores.mean(), 3)}")
    print(f"Standard Deviation kNN Score: {round(knn_train_scores.std(), 3)}")
    
    print("/" * 100)

这将为以下数量的选定要素生成以下值（对于每个所选要素数，其值相似，可以是 1 到 20）：

我不明白为什么“RF Score（训练）”的值如此之高，但在我进行交叉验证时却没有。

你能帮我解决我在这里做错了什么吗？

预测有那么糟糕，是因为我没有“真”y，因为y_test由 NA 组成？