What is “gradient boosting” in machine learning?
a) An ensemble technique that builds models sequentially to minimize errors by focusing on the residuals
b) A method to tune hyperparameters
c) A technique to reduce the dimensionality of input data
d) A method to cluster data points
Answer:
a) An ensemble technique that builds models sequentially to minimize errors by focusing on the residuals
Explanation:
Gradient Boosting is an ensemble learning technique that builds models sequentially, where each new model attempts to correct the errors made by the previous models. The goal is to minimize the residuals (errors) of the model by adding a new model that reduces the residuals of the previous model.
This process continues iteratively, with each new model focusing on the hardest-to-predict data points. Popular gradient boosting algorithms include XGBoost, LightGBM, and CatBoost, which are widely used for structured data tasks such as classification and regression.
Gradient boosting is known for its high accuracy and efficiency, making it a powerful tool for predictive modeling in various domains, including finance, healthcare, and marketing.