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.

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 ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

A diagnostic model based on delayed post-gadolinium enhancement magnetic resonance imaging (DEMRI) improves the accuracy of ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

Melissa Dowd Begg, Stephen Lagakos, On the Consequences of Model Misspecification in Logistic Regression, Environmental Health Perspectives, Vol. 87 (Jul., 1990), pp. 69-75 ... and multidisciplinary ...

Nicholas J. Horton, Nan M. Laird, Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information, Biometrics, Vol. 57, No. 1 (Mar., 2001), pp. 34-42 ...

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 ...