使用 scikit-learn 进行监督机器学习

Question

这是我第一次做监督式机器学习。这是一个非常高级的话题(至少对我来说(，我发现很难指定一个问题，因为我不确定出了什么问题。

# Create a training list and test list (looks something like this):
train = [('this hostel was nice',2),('i hate this hostel',1)]
test = [('had a wonderful time',2),('terrible experience',1)]
# Loading modules
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import metrics
# Use a BOW representation of the reviews
vectorizer = CountVectorizer(stop_words='english') 
train_features = vectorizer.fit_transform([r[0] for r in train]) 
test_features = vectorizer.fit([r[0] for r in test])
# Fit a naive bayes model to the training data
nb = MultinomialNB()
nb.fit(train_features, [r[1] for r in train])
# Use the classifier to predict classification of test dataset
predictions = nb.predict(test_features)
actual=[r[1] for r in test]

在这里我得到错误：

float() argument must be a string or a number, not 'CountVectorizer'

这让我感到困惑，因为我在评论中压缩的原始评级是：

type(ratings_new[0])
int

Answer 1

你应该改变行

test_features = vectorizer.fit([r[0] for r in test])

自：

test_features = vectorizer.transform([r[0] for r in test])

原因是您已经使用训练数据来拟合矢量化器，因此无需在测试数据上再次拟合它。相反，您需要转换它。