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machine learner xgboost  (Kaggle Inc)

 
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    Structured Review

    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
    https://www.bioz.com/result/machine learner xgboost/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    machine learner xgboost - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study"

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

    Journal: BMC Medical Informatics and Decision Making

    doi: 10.1186/s12911-021-01558-y

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

    Techniques Used:

    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
    Figure Legend 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

    Techniques Used:

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

    Techniques Used:



    Similar Products

    machine learner xgboost  (Kaggle Inc)
    90
    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
    https://www.bioz.com/result/machine learner xgboost/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    machine learner xgboost - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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: