<jupyter_start><jupyter_text>逻辑回归在scikit-learn中的实现简介============================== 分析用的代码版本信息：```bash~/W/g/scikit-learn ❯❯❯ git log -n 1commit d161bfaa1a42da75f4940464f7f1c524ef53484fAuthor: <NAME> Date:   Thu May 26 18:36:37 2016 -0400    Add missing double quote (6831)```  0. 总纲下面是sklearn中逻辑回归的构成情况:<jupyter_code>SVG("./res/sklearn_lr.svg")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2037533512064343, 'fraction_numerical': 0.1260053619302949, 'mean_word_length': 8.121951219512194, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '10966', 'license': 'CC0-1.0', 'n_tokens_mistral': 218, 'n_tokens_neox': 193, 'n_words': 23, 'path': 'machine_learning/logistic_regression/sklearn_lr.ipynb', 'repo_name': 'ningchi/book_notes', 'size': '380'}