curve and area under the ROC curve (AUC) represent a logistic regression classifier’s model performance by depicting the trade−off rates between TPs and FPs for given categorization criteria.

the key characteristic of a logistic regression model (Figure 1). The purpose of the function is to transform a probability (as a real number) into a range between 0 and 1 and cannot go beyond this ...

Initially, the researchers reproduced previous results using the same dataset to validate the accuracy and area under the curve (AUC) of the logistic regression models - a measure of goodness of fit.

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

The least absolute shrinkage and selection operator-logistic regression (Lasso-LR) model is optimal for predicting in-hospital mortality for adult patients with alcoholic cirrhosis complicated by ...

When the dependent variable is categorical, a common approach is to use logistic regression, a method that takes its name from the type of curve it uses ... of the logistic model and of ...