Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

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.

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.

As defined on TechTarget, logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a ...

Briefly, a total of 6 experiments are conducted to train weights, and hence, 6 sets of weight coefficients are obtained with 4 from the logistic regression algorithm and 2 from the recommendation ...

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 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 ...

The author proved that once the logistic regression model confirm the effectiveness for a CME, the recommendation algorithm can be used to recommend similar historical events.