While there are a number of other libraries out there to help with gradient boosting or other solutions to help train machine learning systems (XGBoost being one), Bilenko argued that the benefit ...

They found the gradient boosting machine model performed best, with a positive predictive value of 23.1% (95% CI, 20.5-25.9) and negative predictive value of 97.8% (95% CI, 97.4-98.1) when ...

A machine learning gradient boosting regression system, also called a gradient boosting machine (GBM), predicts a single numeric value. A GBM is an ensemble (collection) of simple decision tree ...

Gradient boosting machines (GBMs) are a powerful ensemble learning technique that builds a model incrementally by combining weak models (typically decision trees) to form a strong predictive model.

Notably, the light gradient boosting machine (LGBM) model outperformed others. As a result, the LGBM model was selected as the predictive tool for assessing Gleevec adherence in patients with GIST.

Compared to existing library implementations of gradient boosting regression, a from-scratch implementation allows much easier customization and integration with other .NET systems. By James McCaffrey ...