<commit_before>"""
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Classification of text documents using sparse features
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This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of standard numpy arrays.<commit_msg>better title for the mlcomp example<commit_after>"""
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Classification of text documents: using a MLComp dataset
========================================================

This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of standard numpy arrays.
{'fraction_non_alphanumeric': 0.28554778554778554, 'fraction_numerical': 0.0, 'mean_word_length': 6.8807339449541285, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7582910', 'n_tokens_mistral': 181, 'n_tokens_neox': 164, 'n_words': 91}