<jupyter_start><jupyter_text>RegressionsThe [ElasticNet](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.htmlsklearn.linear_model.ElasticNet) interface from scikit-learn has been used for model selection of the best regression model, where the parameters alpha and l1_ratio are selected by grid search cross validation in the the training dataset. The linear regression, the lasso regression and the ridge regression are particular cases from this model, which are all considered in the model selection procedure.The l1_ratio take values in [0, 1], and represents the ratio between the l1 loss term and the l2 loss term. On the other hand, the alpha parameter is a multiplying constant for l1 and l2 loss terms.<jupyter_code>from src.models.train_model import ModelTrain
ModelTrain("config_regressions.yml").run()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.05815279361459521, 'fraction_numerical': 0.009122006841505131, 'mean_word_length': 6.19672131147541, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '208576', 'license': 'MIT', 'n_tokens_mistral': 239, 'n_tokens_neox': 226, 'n_words': 100, 'path': 'notebooks/data/training/training_regressions.ipynb', 'repo_name': 'esowc/aq-biascorrection', 'size': '877'}