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

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

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

Logistic regression is the appropriate tool for such an investigation. Note that Model Pr{ }: determines which value of the dependent variable the model is based on; usually, the value representing an ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to perform binary classification (predicting a variable with two possible discrete values) using ...

More information: Haoyang Hong et al, simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models, Health Data Science (2023). DOI: 10.34133/hds.0023 ...

The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of ...

Artificial Neural Network(Ann) and logistic regression (LR) models were selected to predict the risk of OILI, and the performance of the two models was evaluated and compared, in the expectation ...

In recent columns we showed how linear regression can be used to predict a continuous dependent variable given other independent variables 1,2. When the dependent variable is categorical, a common ...