Logistic regression was developed starting in the late 1930s as an effort to improve a binary classification technique called probit regression. Even though several more recently developed techniques, ...

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 ...

Basic logistic regression classification is arguably the most fundamental machine learning (ML) technique. Basic logistic regression can be used for binary classification, for example predicting if a ...

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.

The classification boundary is shown by the black line. Logistic Regression attains an accuracy of 0.969 and a F1-score of 0.628.

Correct absence classification was higher with Logistic Regression (73%) than with Overlap Analysis (32%). Overlap Analysis tends to maximise the potential area of occurrence of the species, which ...