The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

This article presents a complete demo program for logistic regression, using batch stochastic gradient descent training with weight decay. Compared to other binary classification techniques, logistic ...

Regression can be used on categorical responses to estimate probabilities and to classify.

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies.

Peiming Wang, Martin L. Puterman, Mixed Logistic Regression Models, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 3, No. 2 (Jun., 1998), pp ...

To account for such an over-dispersion, we propose to use an additive logistic normal multinomial regression model to associate the covariates to bacterial composition. The model can naturally account ...