如何从普通的机器学习技术转变为交叉验证？

Question

from sklearn.svm import LinearSVC

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.feature_extraction.text import TfidfTransformer

from sklearn.metrics import accuracy_score

X = data['Review']

y = data['Category']

tfidf = TfidfVectorizer(ngram_range=(1,1))

classifier = LinearSVC()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)

clf =  Pipeline([
    ('tfidf', tfidf),
    ('clf', classifier)
])

clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

print(classification_report(y_test, y_pred))


accuracy_score(y_test, y_pred)

这是训练模型和预测的代码。我需要知道我的模型性能。那么我应该在哪里改成cross_val_score?

Answer 1

来自 sklearn documentation

The simplest way to use cross-validation is to call the cross_val_score helper function on the estimator and the dataset.

你的情况是

from sklearn.model_selection import cross_val_score
scores = cross_val_score(clf, X_train, y_train, cv=5)
print(scores)

Answer 2

使用这个：（这是我之前项目的一个例子）

import numpy as np
from sklearn.model_selection import KFold, cross_val_score

kfolds = KFold(n_splits=5, shuffle=True, random_state=42)
def cv_f1(model, X, y):
  score = np.mean(cross_val_score(model, X, y,
                                scoring="f1",
                                cv=kfolds))
  return (score)


model = ....

score_f1 = cv_f1(model, X_train, y_train)

你可以有多个得分。您应该只更改 scoring="f1"。如果您想查看每次折叠的分数，只需删除 np.mean

如何从普通的机器学习技术转变为交叉验证？

How to change from normal machine learning technique to cross validation?

python

machine-learning

scikit-learn

cross-validation

data-science