Master Boosting in Machine Learning

Boosting is an ensemble technique that combines multiple weak classifiers to create a strong classifier. It builds models sequentially, with each new model correcting errors made by the previous one. The technique assigns higher weights to misclassified data points and lower weights to correctly classified ones. The model focuses on harder-to-learn examples by training on weighted data. This process continues until the dataset is fully predicted or a predefined number of models is reached.

For more details, please read the GeeksforGeeks article: Bagging vs Boosting in Machine Learning.