What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.

Bayesian Additive Regression Trees (BART) is a nonparametric ensemble method that models complex relationships by summing a collection of decision trees, each operating as a weak learner.

The CART analysis revealed complex interaction effects previously unobserved in the logistic regression. Comparisons of CART with traditional statistical approaches and other tree-based programs are ...

For more information on this research see: Comparison and Analysis of the Effectiveness of Linear Regression, Decision Tree, and Random Forest Models for Health Insurance Premium Forecasting.

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

Regression trees are applied to evaluate system performance – using two water quality and two economic performance metrics. Regression trees facilitated insights into the significance of uncertain ...

Following decision tree generation, we performed multivariable logistic regression on the variables included in the CART analysis to assess their individual effect size estimation with the occurrence ...