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receiver operating characteristic roc curve  (STATA Corporation)


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    STATA Corporation receiver operating characteristic roc curve
    Receiver Operating Characteristic Roc Curve, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 35482 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/receiver+operating+characteristic+roc+curves/pm40079894-31-64-89?v=STATA+Corporation
    Average 99 stars, based on 35482 article reviews
    receiver operating characteristic roc curve - by Bioz Stars, 2026-07
    99/100 stars

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    <t>Receiver</t> operator characteristic curve <t>(ROC)</t> of the LASSO regression (LASSO), classification tree (CT), random forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) models for the train datasets (A) and test datasets (B). The area under the curve (AUC) of the ROC is represented by the value in parentheses.
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    Receiver operator characteristic curve (ROC) of the LASSO regression (LASSO), classification tree (CT), random forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) models for the train datasets (A) and test datasets (B). The area under the curve (AUC) of the ROC is represented by the value in parentheses.

    Journal: Poultry Science

    Article Title: Predictive modeling of bruising in broiler chickens using machine learning algorithms

    doi: 10.1016/j.psj.2025.105756

    Figure Lengend Snippet: Receiver operator characteristic curve (ROC) of the LASSO regression (LASSO), classification tree (CT), random forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) models for the train datasets (A) and test datasets (B). The area under the curve (AUC) of the ROC is represented by the value in parentheses.

    Article Snippet: Receiver operator characteristic curve (ROC) of the LASSO regression (LASSO), classification tree (CT), random forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) models for the train datasets (A) and test datasets (B).

    Techniques: Plasmid Preparation