Logistic regression. Linear regression. Outcome variable . Models binary outcome variables. Models continuous outcome variables. Regression line. Fits a non-linear S-curve using the sigmoid function .

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 ...

If the signal to noise ratio is low (it is a ‘hard’ problem) logistic regression is likely to perform best. In technical terms, if the AUC of the best model is below 0.8, logistic very clearly ...

David W. Hosmer, Borko Jovanovic, Stanley Lemeshow, Best Subsets Logistic Regression, Biometrics, Vol. 45, No. 4 (Dec., 1989), pp. 1265-1270. Free online reading for over 10 million articles; Save and ...

Often, regression models that appear nonlinear upon first glance are actually linear. The curve estimation procedure can be used to identify the nature of the functional relationships at play in ...

Collapsibility of Logistic Regression Coefficients. Jianhua Guo and Zhi Geng. Journal of the Royal Statistical Society. Series B (Methodological) Vol. 57, No. 1 (1995), pp. 263-267 (5 pages) Published ...

A variable undergoing logistic growth initially grows exponentially. After some time, the rate of growth decreases and the function levels off, forming a sigmoid, or s-shaped curve. For example ...