What is “Boosting” in machine learning?
a) An ensemble learning technique that builds models sequentially, each correcting the errors of the previous model
b) A method to tune hyperparameters
c) A technique for reducing the size of a dataset
d) A way to normalize data
Answer:
a) An ensemble learning technique that builds models sequentially, each correcting the errors of the previous model
Explanation:
Boosting is an ensemble learning technique where models are trained sequentially, and each new model attempts to correct the errors made by the previous models. The idea is to focus on the hardest-to-predict examples by giving them more weight in subsequent iterations.
Algorithms like AdaBoost and Gradient Boosting are popular examples of boosting techniques. These models often perform well in terms of accuracy, especially on complex datasets, by reducing both bias and variance.
Boosting is widely used in applications where accuracy is paramount, such as fraud detection, image classification, and natural language processing.