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Gradient boosting builds models sequentially to correct errors made by previous models from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Gradient boosting is a powerful machine learning technique that aims to improve the performance of a model by learning from the errors of previous models. The idea is to build a series of models sequentially, with each new model focusing on correcting the errors made by the previous one. In essence, gradient boosting works by combining the predictions of multiple weak learners to create a strong learner that is capable of making accurate predictions. The process begins with the first model, which is typically a simple model that makes predictions based on the available data. The errors made by this model are then used to train the next model in the sequence. This second model is designe...
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