脊分类器：解释mod的答案

Question

我训练过这个模型，在某些情况下我无法解释答案

我创造了玩具火车样品

CourtIDGAS    Addr_upd
03MS0001      usa, new-york, times square, 1
03MS0001      usa, new-york, times square, 3
03MS0001      usa, new-york, times square, 5
03MS0001      usa, new-york, times square, 7
03MS0001      usa, new-york, times square, 9
03MS0001      usa, new-york, times square, 2
03MS0001      usa, new-york, times square, 4
03MS0001      usa, new-york, times square, 6
03MS0001      usa, new-york, times square, 8
03MS0001      usa, new-york, times square, 10
03MS0001      usa, new-york, times square, 12
03MS0002      usa, new-york, times square, 11
03MS0002      usa, new-york, times square, 13
03MS0002      usa, new-york, times square, 14
03MS0002      usa, new-york, times square, 16

我使用CountVectorizer将文本转换为向量，并使用RidgeClassifier预测地址的类别

vec = CountVectorizer(token_pattern='(?u)\\b[а-яё0-9\/\-]+\\b', min_df=1)
X = vec.fit_transform(df.Addr_upd)
Y = df["CourtIDGAS"]
clf = RidgeClassifier(random_state=42)
clf.fit(X, y)

当我试着从火车样本中预测smth时，我得到了正确的答案 但是当我尝试用另一个数据进行预测时，例如usa, new-york, times square, 18，我得到了类03MS0001

我无法解释这一点，因为词汇表中的最大值是16，但这个例子在我心目中更接近03MS0002

如何解释这个量词的答案？ 如何正确处理这些数据