如何使用交叉验证测试看不见的测试数据并预测标签？

Question

1.包含数据（即文本描述）以及分类标签的CSV

df = pd.read_csv('./output/csv_sanitized_16_.csv', dtype=str)
X = df['description_plus']
y = df['category_id']

2.此 CSV 包含需要预测标签的看不见的数据（即文本描述）

df_2 = pd.read_csv('./output/csv_sanitized_2.csv', dtype=str)
X2 = df_2['description_plus']

对上述训练数据（项目 #1）进行操作的交叉验证函数。

def cross_val():
    cv = KFold(n_splits=20)
    vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                     stop_words='english')
    X_train = vectorizer.fit_transform(X) 
    clf = make_pipeline(preprocessing.StandardScaler(with_mean=False), svm.SVC(C=1))
    scores = cross_val_score(clf, X_train, y, cv=cv)
    print(scores)
    print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
cross_val()

我需要知道如何将看不见的数据（项目 #2）传递给交叉验证函数以及如何预测标签？

Answer 1

使用scores = cross_val_score(clf, X_train, y, cv=cv)只能得到模型的交叉验证分数。 cross_val_score 将根据cv 参数在内部将数据拆分为训练和测试。

因此，您获得的值是 SVC 的交叉验证准确性。

要获得看不见的数据的分数，您可以首先拟合模型，例如

clf = make_pipeline(preprocessing.StandardScaler(with_mean=False), svm.SVC(C=1))
clf.fit(X_train, y) # the model is trained now

然后做clf.score(X_unseen,y)

最后一个将返回模型在看不见的数据上的准确性。

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编辑：做你想做的最好的方法是使用 GridSearch 首先使用训练数据找到最佳模型，然后使用看不见的（测试）数据评估最佳模型：

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

# load some data
iris = datasets.load_iris()
X, y = iris.data, iris.target

#split data to training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# hyperparameter tunig of the SVC model
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svc = svm.SVC()

# fit the GridSearch using the TRAINING data
grid_searcher = GridSearchCV(svc, parameters)
grid_searcher.fit(X_train, y_train)

#recover the best estimator (best parameters for the SVC, based on the GridSearch)
best_SVC_model = grid_searcher.best_estimator_

# Now, check how this best model behaves on the test set
cv_scores_on_unseen = cross_val_score(best_SVC_model, X_test, y_test, cv=5)
print(cv_scores_on_unseen.mean())