<jupyter_start><jupyter_text>AUTO ML<jupyter_code># pip install TPOT
from tpot import TPOTRegressor
tpot_model = TPOTRegressor(generations = 10, population_size = 10, max_eval_time_mins = 0.1, max_time_mins = 1, verbosity = 2)
tpot_model.fit(X_train, y_train)
tpot_model.export('tpot_ml_advertising.py')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1021021021021021, 'fraction_numerical': 0.024024024024024024, 'mean_word_length': 9.121212121212121, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '56938', 'license': 'MIT', 'n_tokens_mistral': 148, 'n_tokens_neox': 135, 'n_words': 23, 'path': 'model_generator.ipynb', 'repo_name': 'shienasangalang/ftw-webapp-deployment', 'size': '333'}