A machine-learning algorithm can use demographic, symptomatic, and clinical data to accurately predict the health-related quality of life (QoL) of patients with kidney stones, new research shows.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the gradient boosting regression technique, where the goal is to predict a single numeric value. Compared to ...

XGBoost is a popular open source machine learning library that can be used to solve all kinds of prediction problems. Here’s how to use XGBoost with InfluxDB. XGBoost is an open source machine ...

Investigators created a model for predicting the risk of urinary sepsis after endoscopic surgery for urinary stones using several key variables. A model incorporating values of stone composition and ...