使用 scikit-learn 进行监督式机器学习

Question

这是我第一次做有监督的机器学习。这是一个非常高级的主题（至少对我而言），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]

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，因为我压缩在评论中的原始评分是：

type(ratings_new[0])
int

Answer 1

你应该换行

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

至：

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

原因是您已经使用了训练数据来拟合向量化器，因此您不需要在测试数据上再次进行拟合。相反，您需要对其进行转换。

使用 scikit-learn 进行监督式机器学习

Supervised machine learning with scikit-learn

python

scikit-learn

supervised-learning