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)

这是用于训练模型和预测的代码。我需要知道我的模型性能。那么我应该在哪里更改为cross_val_score？

Answer 1

使用这个：(这是我以前项目的一个例子(

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)

你可以多次得分。您应该将scoring更改为"f1"。如果你想看到每个折叠的分数，只需删除np.mean

Answer 2

来自sklearn文档

使用交叉验证的最简单方法是在估计器和数据集上调用cross_val_score辅助函数。

在您的情况下，它将是

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