title: ValueError: Found input variables with inconsistent numbers of samples on binary SVM
body: <p>Trying to run a binary SVM on on the 20_newsgroups dataset. Seem to be getting a ValueError: Found input variables with inconsistent numbers of samples: [783, 1177]. Can anyone suggest why this is happening? </p>
  
  <pre><code>from sklearn.datasets import fetch_20newsgroups
  from nltk.corpus import names
  from nltk.stem import WordNetLemmatizer
  # from sklearn.feature_extraction.text import CountVectorizer
  import numpy as np
  import pandas as pd
  categories = ["comp.graphics", 'sci.space']
  data_train = fetch_20newsgroups(subset='train', categories=categories, random_state=42)
  data_test = fetch_20newsgroups(subset='test', categories=categories, random_state=42)
  
  def is_letter_only(word) : 
      return word.isalpha()
  all_names = set (names.words())
  lemmatizer = WordNetLemmatizer()
  def clean_text(docs) : 
      docs_cleaned = []
      for doc in docs:
          doc = doc.lower()
          doc_cleaned = ' '.join(lemmatizer.lemmatize(word)
                  for word in doc.split() if is_letter_only(word)
                  and word not in all_names)
          docs_cleaned.append(doc_cleaned)
      return docs_cleaned
  
  cleaned_train = clean_text(data_train.data)
  label_train = data_train.target
  cleaned_test = clean_text(data_train.data)
  label_test = data_test.target
  len(label_train),len(label_test)
  
  from sklearn.feature_extraction.text import TfidfVectorizer
  tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=None)
  term_docs_train = tfidf_vectorizer.fit_transform(cleaned_train)
  term_docs_test = tfidf_vectorizer.transform(cleaned_test)
  
  from sklearn.svm import SVC
  svm = SVC(kernel='linear', C=1.0, random_state=42)
  
  svm.fit(term_docs_train, label_train)
  
  accuracy = svm.score(term_docs_test, label_test)
  print(accuracy)
  
  </code></pre>