But there is also some empirical work comparing various algorithms across many datasets and drawing some conclusions, what types of problems tend to do better with trees vs logistic regression.

Kin-Yee Chan, Wei-Yin Loh, LOTUS: An Algorithm for Building Accurate and Comprehensible Logistic Regression Trees, Journal of Computational and Graphical Statistics, Vol. 13, No. 4 (Dec., 2004), pp.

This is a type of an ML method utilized to predict data value based on prior observations of data sets. Logistic regression can be: Binary: the categorical response has only two possible outcomes ...

Infinite parameter estimates in logistic regression are commonly thought of as a problem. This article shows that in principle an analyst should be happy to have an infinite slope in logistic ...

The sensitivity of the logistic regression algorithm was 76% and the specificity was 87% and was deemed more suitable for the classification of melanoma dermoscopic images over the support vector ...

We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of ...