SportsCategorizer

from sklearn.datasets import fetch_20newsgroups
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

train_emails = fetch_20newsgroups(categories = ['comp.sys.ibm.pc.hardware', 'rec.sport.hockey'],subset='train',shuffle='true',random_state=108)
test_emails = fetch_20newsgroups(categories = ['comp.sys.ibm.pc.hardware', 'rec.sport.hockey'],subset='test',shuffle='true',random_state=108)
counter = CountVectorizer()
counter.fit(test_emails.data + train_emails.data)
train_counts = counter.transform(train_emails.data)
test_counts = counter.transform(test_emails.data)
classifier = MultinomialNB()
classifier.fit(train_counts, train_emails.target)
print(classifier.score(test_counts, test_emails.target))