如何使用交叉验证和预测标签来测试看不见的测试数据

Question

1.包含数据(即文本描述(和分类标签的CSV

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

2.此CSV包含未看到的数据(即文本描述(，需要对其标签进行预测

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功能。

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)只能获得模型的交叉验证分数。cross_val_score将根据cv参数在内部将数据划分为训练和测试。

因此，您得到的值是SVC的交叉验证精度。

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

然后进行clf.score(X_unseen,y)

最后一个将返回模型对看不见的数据的准确性。

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编辑：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())