<jupyter_start><jupyter_text>General Population<jupyter_code>best_clf = xgBoost(co_train_gpop, out_train_death_gpop)
cross_val(co_train_gpop, out_train_death_gpop)
print()
scores(co_validation_gpop, out_validation_death_gpop)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits
[23:07:38] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.<jupyter_text>High Continuity<jupyter_code>best_clf = xgBoost(co_train_high, out_train_death_high)
cross_val(co_train_high, out_train_death_high)
print()
scores(co_validation_high, out_validation_death_high)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits
[00:02:19] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.<jupyter_text>Low Continuity<jupyter_code>best_clf = xgBoost(co_train_low, out_train_death_low)
cross_val(co_train_low, out_train_death_low)
print()
scores(co_validation_low, out_validation_death_low)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits
[00:25:22] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
{'fraction_non_alphanumeric': 0.08307501549907005, 'fraction_numerical': 0.03161810291382517, 'mean_word_length': 7.771739130434782, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 10, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '478069', 'license': 'MIT', 'n_tokens_mistral': 603, 'n_tokens_neox': 547, 'n_words': 154, 'path': 'EHR_Only/GBT/Death_No.ipynb', 'repo_name': 'shreyaskar123/EHR-Discontinuity', 'size': '1613'}