title: Which algorithms to use for one class classification?
body: <p>I have over 15000 text docs of a specific topic. I would like to build a language model based on the former so that I can present to this model new random text documents of various topics and the algorithms tells if the new doc is of the same topic.</p>
  
  <p>I tried out <code>sklearn.naive_bayes.MultinomialNB</code>, <code>sklearn.svm.classes.LinearSVC</code> and others, however I have the following problem:</p>
  
  <p>These algorithms require training data with more than one label or category and I only have web pages of covering a specific topic. The other docs are not labeled and of many different topics.</p>
  
  <p>I would appreciate any guidance on how to train a model with only one label or how to proceed in general. What I have so far is:</p>
  
  <pre><code>c = MultinomialNB()
  c.fit(X_train, y_train)
  c.predict(X_test)
  </code></pre>
  
  <p>Thank you very much.</p>