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.

First off, you need to be clear what exactly you mean by advantages. People have argued the relative benefits of trees vs. logistic regression in the context of interpretability, robustness, etc.

Nonlinear regression algorithms, which fit curves that are not linear in their parameters to data, are a little more complicated, because, unlike linear regression problems, they can’t be solved ...

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 ...

“The region-growing segmentation algorithm incorporated with the logistic regression classifier can achieve a better performance compared with the other algorithms in this review,” Xiaoyu Cui ...

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 ...

Scott Menard, Six Approaches to Calculating Standardized Logistic Regression Coefficients, The American Statistician, Vol. 58, No. 3 (Aug., 2004), pp. 218-223 ...