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 ...

The random forest model significantly outperformed all other models, including the logistic regression model that the entire paper focuses on, with an eventual AUC of 0.936 and an accuracy of 0.918.

Some results are presented on improving the fit of the logistic regression model for binary data by transforming the vector of explanatory variables. The methods are based on consideration of the ...

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.

Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail ...

A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent ...