<jupyter_start><jupyter_text>General Population<jupyter_code>best_clf = xgBoost(co_train_gpop, out_train_hemorrhage_gpop)
scores(co_train_gpop, out_train_hemorrhage_gpop)
print()
scores(co_validation_gpop, out_validation_hemorrhage_gpop)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits<jupyter_text>High Continuity<jupyter_code>best_clf = xgBoost(co_train_high, out_train_hemorrhage_high)
scores(co_train_high, out_train_hemorrhage_high)
print()
scores(co_validation_high, out_validation_hemorrhage_high)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits<jupyter_text>Low Continuity<jupyter_code>best_clf = xgBoost(co_train_low, out_train_hemorrhage_low)
scores(co_train_low, out_train_hemorrhage_low)
print()
scores(co_validation_low, out_validation_hemorrhage_low)<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '273537', 'license': 'MIT', 'n_tokens_mistral': 368, 'n_tokens_neox': 333, 'n_words': 55, 'path': 'EHR_Claims/GBT/EHR_C_Hemorrhage_No.ipynb', 'repo_name': 'shreyaskar123/EHR-Discontinuity', 'size': '893'}