Gradient Boosting Machines (GBM)
An ensemble learning method that builds models sequentially, with each new model correcting errors made by the previous ones, often used in predictive analytics.
Implications
A machine learning technique used for regression and classification tasks that builds models in a sequential manner, where each new model attempts to correct the errors of the previous ones, often leading to highly accurate predictive models.
Example
Example: A financial institution uses Gradient Boosting Machines to predict customer credit risk, improving accuracy in identifying high-risk customers compared to traditional linear models.
Related Terms
Different from Random Forests, which build multiple independent trees, GBM builds trees sequentially, each one correcting the errors of the previous ones, leading to a more refined model.
Get INSTANT comprehensive market research of any industry
Chat with AII Consultant Now!
