When training a logistic regression model, there are many optimization algorithms that can be used, such as stochastic gradient descent (SGD), iterated Newton-Raphson, Nelder-Mead and L-BFGS. This ...

Basic logistic regression can be used for binary classification, for example predicting if a person is male or female based on predictors such as age, height, annual income, and so on. Multi-class ...

(b) The effect of outliers on classification based on step and logistic regression. Regression using step and logistic models yields thresholds of 185 cm (solid vertical blue line) and 194 cm ...

Binary logistic regression: also referred to as binomial or simply logistic regression, this is when the outcome variable has two categories (e.g. death, ... In machine learning, it is used mainly as ...

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.

Logistic Regression attains an accuracy of 0.969 and a F1-score of 0.628. Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens ...

Logistic regression. Classification algorithms can find solutions to supervised learning problems that ask for a choice (or determination of probability) between two or more classes.

What are the advantages of logistic regression over decision trees? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better ...