What is gradient boosting in machine learning?

What is gradient boosting in machine learning?

Gradient boosting is a machine learning technique used for regression and classification problems. It builds models in a sequential manner, where each new model corrects the errors made by the previous ones.

How does gradient boosting differ from regular boosting?

While both are ensemble methods, gradient boosting optimizes a loss function using gradient descent, whereas regular boosting focuses on adjusting weights of misclassified instances.

Can you give me an example of how gradient boosting works?

Sure! Imagine you have a dataset with features and a target variable. Gradient boosting starts with a simple model, like a decision tree, and then adds more trees that focus on the errors of the previous trees, gradually improving the overall prediction.

What are some popular libraries for implementing gradient boosting?

Some popular libraries include XGBoost, LightGBM, and CatBoost. They offer efficient implementations and are widely used in data science competitions.