… Firstly we aim to present solid evidence on the performance of standard logistic regression
and random forests with default values. Secondly, we demonstrate the design of a benchmark …

… It should be noted this work only investigates random forest and logistic regression,
however generalization of the current application can be adapted to other linear and nonlinear …

… Random Forests and logistic regression is divided into two parts. The first, presented in this
section, evaluates the ability of Random Forests … of Random Forests with logistic regression …

… This study aims to compare RandomForest machine learning algorithm and Logistic Regression
algorithm towards the prediction of diabetes. We analyzed 66,325 records that extracted …

… Random Forests [4] in this paper. Furthermore, we compare the methods with a Bayesian
logistic … study include LogicFS, MCLR, GPAS, MLR-GEP, RF, and Bayesian logistic regression. …

… namely logistic regression, random forest and K-nearest neighbour. Logistic regression is
used … Random forest works on decision trees which are used to classify new object from input …

… (Random Forest) to account for interactions between them. We also intended to compare
Random Forest with traditional Logistic regression… selected by using Random Forest technique. …

… The main purpose of the present study is to use three state-of-the-art data mining techniques,
namely, logistic model tree (LMT), random forest (RF), and classification and regression …

… In this work, we compare the performance of random forest and logistic regression on the
prediction of an imbalanced dataset. We propose several ways to enhance two models based …

… We decided to make a comparison and include a less investigated Logistic Regression
classification method, because it is still used in practical tasks as one of the most accurate …