What Is the Difference Between Gradient Descent and Gradient Boosting?

Answer: Gradient descent is an optimization algorithm used for minimizing a loss function, while gradient boosting is a machine learning technique that combines weak learners (typically decision trees) iteratively to improve predictive performance.

Gradient Descent vs Gradient Boosting: Comparison

Aspect	Gradient Descent	Gradient Boosting
Objective	Minimizing a loss function	Building an ensemble model
Main Usage	Optimization algorithm for model training	Machine learning technique for building ensemble models
Optimization Type	Iteratively updates parameters in the direction of descent	Sequentially adds weak learners to minimize the residual errors
Loss Function	Directly minimizes a specified loss function	Indirectly minimizes the residual errors of previous models
Examples	Linear regression, logistic regression, neural networks	XGBoost, LightGBM, CatBoost
Algorithm Types	Batch Gradient Descent, Stochastic Gradient Descent, Mini-batch Gradient Descent	Gradient Boosting Machines (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), CatBoost

In summary, while both gradient descent and gradient boosting involve the use of gradients to improve model performance, gradient descent is an optimization algorithm used to minimize a loss function directly, while gradient boosting is a machine learning technique that combines weak learners to iteratively improve predictive accuracy.