The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity.

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

Many machine learning code libraries have built-in multi-class logistic regression functionality. However, coding multi-class logistic regression from scratch has least four advantages over using a ...

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 ...

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

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

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 ...

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual ...

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.