<jupyter_start><jupyter_text>General Population<jupyter_code>best_clf = xgBoost(co_train_gpop, out_train_hemorrhage_gpop)
prediction_gpop_low = best_clf.predict_proba(co_validation_low)
prediction_gpop_high = best_clf.predict_proba(co_validation_high)
#cross_val(co_train_gpop, out_train_hemorrhage_gpop)

#print()

#scores(co_validation_gpop, out_validation_hemorrhage_gpop)
g_pred = best_clf.predict_proba(co_validation_gpop)[:,1]<jupyter_output>Fitting 5 folds for each of 4 candidates, totalling 20 fits
[00:30:00] 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_hemorrhage_high)

cross_val(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
[10:24:17] 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_hemorrhage_low)

cross_val(co_train_low, out_train_hemorrhage_low)

print()

scores(co_validation_low, out_validation_hemorrhage_low)
for i in g_pred:
    print(i,",")<jupyter_output>0.070383556 ,
0.05144859 ,
0.033512607 ,
0.10411501 ,
0.07879658 ,
0.034040865 ,
0.07909436 ,
0.034892324 ,
0.061165646 ,
0.044821277 ,
0.13714111 ,
0.07484187 ,
0.03386537 ,
0.1317113 ,
0.07925005 ,
0.03296707 ,
0.06051837 ,
0.061743163 ,
0.057106882 ,
0.2200395 ,
0.061645202 ,
0.09240291 ,
0.03031238 ,
0.027899623 ,
0.07484187 ,
0.05895569 ,
0.063316435 ,
0.085640155 ,
0.053986866 ,
0.094907954 ,
0.09781416 ,
0.05101261 ,
0.18233001 ,
0.047538813 ,
0.05505465 ,
0.0552596 ,
0.09144963 ,
0.19796737 ,
0.09359699 ,
0.09726568 ,
0.061416835 ,
0.03452647 ,
0.3765118 ,
0.07392579 ,
0.047146227 ,
0.06884276 ,
0.13837774 ,
0.054597612 ,
0.17145032 ,
0.07257319 ,
0.052723974 ,
0.10227429 ,
0.039438747 ,
0.10380834 ,
0.022440597 ,
0.07121517 ,
0.04271937 ,
0.07879658 ,
0.049210552 ,
0.061986834 ,
0.1424551 ,
0.084126465 ,
0.12325959 ,
0.026681542 ,
0.045838226 ,
0.14143418 ,
0.13066092 ,
0.0482718 ,
0.05530674 ,
0.06917472 ,
0.031700976 ,
0.09781416 ,
0.09632857 ,
0.079651445 ,
0.03878603 ,
0.0[...]
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