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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