<jupyter_start><jupyter_text>Decision tree<jupyter_code>clf = tree.DecisionTreeClassifier()
scores = cross_val_score(clf, X, y, scoring = 'accuracy', cv = 10)
print(scores)    
print(sum(scores)/len(scores))<jupyter_output>0.5757772401644059
{'fraction_non_alphanumeric': 0.13692946058091288, 'fraction_numerical': 0.07883817427385892, 'mean_word_length': 9.083333333333334, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '293778', 'license': 'MIT', 'n_tokens_mistral': 113, 'n_tokens_neox': 97, 'n_words': 13, 'path': 'Lab-6-Decision-Tree/news_category_classification.ipynb', 'repo_name': 'Abhishek-dev2/Artificial-Intelligence-Lab', 'size': '241'}