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machine learner xgboost  (Kaggle Inc)
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Kaggle Inc machine learner xgboost
Under- and overtriage rates of logistic regression and <t> XGBoost </t> (median and 2.5 and 97.5 percentiles (calculation on 1000 runs))
Machine Learner Xgboost, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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machine learner xgboost - by Bioz Stars, 2026-04
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Under- and overtriage rates of logistic regression and XGBoost (median and 2.5 and 97.5 percentiles (calculation on 1000 runs))

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Under- and overtriage rates of logistic regression and XGBoost (median and 2.5 and 97.5 percentiles (calculation on 1000 runs))

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques:

Under- and overtriage rates (median, IQR (Q1-Q3), Q1-1,5IQR & Q3 + 1,5IQR) for logistic regression and XGBoost in the SweTrau and NTDB cohorts. NTDB, National Trauma Data Bank; SweTrau, Swedish Trauma Registry

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Under- and overtriage rates (median, IQR (Q1-Q3), Q1-1,5IQR & Q3 + 1,5IQR) for logistic regression and XGBoost in the SweTrau and NTDB cohorts. NTDB, National Trauma Data Bank; SweTrau, Swedish Trauma Registry

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques:

Median and 2.5 and 97.5 percentiles of difference in under- and overtriage rates between learners (calculation on 1000 runs)

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Median and 2.5 and 97.5 percentiles of difference in under- and overtriage rates between learners (calculation on 1000 runs)

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques: