Using counts and tfidf as features with scikit learn
The error didn't come from the FeatureUnion, it came from the TfidfTransformer
You should use TfidfVectorizer instead of TfidfTransformer, the transformer expects a numpy array as input and not plaintext, hence the TypeError
Also your test sentence is too small for Tfidf testing so try using a bigger one, here's an example:
from nltk.corpus import brownfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizerfrom sklearn.pipeline import FeatureUnionfrom sklearn.naive_bayes import MultinomialNB# Let's get more text from NLTKtext = [" ".join(i) for i in brown.sents()[:100]]# I'm just gonna assign random tags.labels = ['yes']*50 + ['no']*50count_vectorizer = CountVectorizer(stop_words="english", min_df=3)tf_transformer = TfidfVectorizer(use_idf=True)combined_features = FeatureUnion([("counts", count_vectorizer), ("tfidf", tf_transformer)]).fit_transform(text)classifier = MultinomialNB()classifier.fit(combined_features, labels)