.. _example_grid_search_text_feature_extraction.py:


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Sample pipeline for text feature extraction and evaluation
==========================================================

The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the document
classification example.

You can adjust the number of categories by giving there name to the dataset
loader or setting them to None to get the 20 of them.

Here is a sample output of a run on a quad-core machine::

  Loading 20 newsgroups dataset for categories:
  ['alt.atheism', 'talk.religion.misc']
  1427 documents
  2 categories

  Performing grid search...
  pipeline: ['vect', 'tfidf', 'clf']
  parameters:
  {'clf__alpha': (1.0000000000000001e-05, 9.9999999999999995e-07),
   'clf__n_iter': (10, 50, 80),
   'clf__penalty': ('l2', 'elasticnet'),
   'tfidf__use_idf': (True, False),
   'vect__analyzer__max_n': (1, 2),
   'vect__max_df': (0.5, 0.75, 1.0),
   'vect__max_features': (None, 5000, 10000, 50000)}
  done in 1737.030s

  Best score: 0.940
  Best parameters set:
      clf__alpha: 9.9999999999999995e-07
      clf__n_iter: 50
      clf__penalty: 'elasticnet'
      tfidf__use_idf: True
      vect__analyzer__max_n: 2
      vect__max_df: 0.75
      vect__max_features: 50000



**Python source code:** :download:`grid_search_text_feature_extraction.py <grid_search_text_feature_extraction.py>`

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