Want to understand logistic regression? Explore our guide to learn its applications and advantages in data analysis.

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...

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 ...

A multivariate logistic regression model based on 3T mpMRI, clinical, and biopsy parameters for the prediction of prostate cancer extracapsular extension.

A new global test statistic for models with continuous covariates and binary response is introduced. The test statistic is based on nonparametric kernel methods.

The Data Science Lab How to Do Logistic Regression Using ML.NET Microsoft Research's Dr. James McCaffrey show how to perform binary classification with logistic regression using the Microsoft ML.NET ...

Multivariate adaptive regression splines (MARS) have useful features to effectively reduce the number of terms in a model.

Logistic regression models are commonly used to study the association between a binary response variable and an exposure variable. Besides the exposure of interest, other covariates are frequently ...

Understand logistic regression, a key statistical method for relationships with binary outcomes. Explore its formula, assumptions and practical applications.