<jupyter_start><jupyter_text>y = -0.42938013x + 0.85608683x + -2.32815285x + -1.02071026x + 9.59147753<jupyter_code>log.predict_proba(x_train[100:104])
log.predict(x_train[100:104])
0.17264465+0.82305126+0.00430409
from sklearn import metrics
y_pred = log.predict(x_train)
y_pred.shape, y_train.shape
y_pred
y_train
print(metrics.classification_report(y_train, y_pred))
metrics.confusion_matrix(y_train, y_pred)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1292517006802721, 'fraction_numerical': 0.19047619047619047, 'mean_word_length': 8.608695652173912, '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': '209678', 'license': 'Apache-2.0', 'n_tokens_mistral': 243, 'n_tokens_neox': 190, 'n_words': 22, 'path': 'iris_logisticregression.ipynb', 'repo_name': '00FFEF/test_machinelearning', 'size': '441'}