.. _example_grid_search_text_feature_extraction.py: ========================================================== 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 their names 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__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__max_n: 2 vect__max_df: 0.75 vect__max_features: 50000 **Python source code:** :download:`grid_search_text_feature_extraction.py ` .. literalinclude:: grid_search_text_feature_extraction.py :lines: 43-