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.

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 ...

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

ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest model were developed to predict postoperative recurrence. Model performance was ...

Recent scientific article explores the use of machine learning techniques to identify the key risk factors associated with ...

New research is shaking up how we think about evolution, suggesting there's a level of predictability influenced by genes and ...

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 ...

Study published in The Journal of Thoracic and Cardiovascular Surgery indicates OneBreath™ technology can help diagnose and predict pneumonia from a single exhale. "Exhaled breath is an extraordinary ...

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