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 see if there's a relationship between two variables, with the first known ...

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 ...

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 ...

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 ...

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," ...

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

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.