Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable ...

Compared to separate binary regression models, one of the advantages of ordinal logistic regression is that it includes fewer unknown quantities, here odds ratios, in the model. This results in a ...

Ordinal and binary logistic regression models were used to examine the effects of metal exposure on the WMH burden, deep white matter hyperintensity, and periventricular white matter hyperintensity.

When constructing a classification function for high-dimensional data using a basis function model, a huge number of ... effectively minimize squared loss and logistic loss. To make ALS applicable to ...

Implementing binary / multiple logistic regression models, for the well known mnist dataset while also creating the support vector machine(SVM) models ...

Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. Logistic Regression ...

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 this paper, we proposed a framework, called Mulr4FL, for fault localization using a multivariate logistic regression model that combined both static and dynamic features collected from the program ...