Gradient Clipping
Machine Learning
Ensemble models that build sequential trees to minimize errors.
Detailed Explanation
Gradient Boosting Machines (GBMs) are powerful ensemble learning algorithms that build models sequentially, with each new tree aiming to correct the errors of the previous ones. By optimizing a differentiable loss function, GBMs enhance predictive accuracy, making them effective for various tasks such as classification and regression. They are widely used for their robustness and high-performance results.
Use Cases
- •Use case: Improving credit scoring accuracy by sequentially correcting prediction errors with Gradient Boosting Machines.
Related Terms
Other terms in the Machine Learning category