Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584.

Background There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior ...

On the other hand, random forest and bagging tree regression models seem to have a good reputation among machine learning practitioners (most of my colleagues at least) because the models often work ...

Ophthalmology Times connects eye care professionals with surgery, imaging, gene therapy, & diagnostic advances to enhance clinical and patient care.

David Muchlinski, David Siroky, Jingrui He, Matthew Kocher, Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data, Political Analysis, Vol. 24, No. 1 ...

In an age where data drives decisions and automation defines excellence, the insurance industry stands at the cusp of a digital renaissance. At the ...

The global shift toward digital banking has been dramatic, with the volume of cashless transactions increasing year over year. While this growth signals progress in financial technology, it has also ...

Recent study focused on predicting short birth intervals (defined as less than 33 months) among reproductive-age women in ...