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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 designed to focus on the areas where the first model struggled, with the goal of reducing the overall error in the predictions. This process is repeated for a specified number of iterations, with each new model refining the predictions of the previous models. One key aspect of gradient boosting is the use of a loss function to measure the error between the predicted values and the actual values. By minimizing this loss function, the models are able to learn from their mistakes and improve their predictions over time. Additionally, gradient boosting allows for the use of different types of weak learners, such as decision trees, which can be combined to create a more accurate and robust model.- Gradient boosting is a versatile and effective technique for building predictive models that can handle complex relationships in the data. By learning from the errors of previous models and iteratively improving the predictions, gradient boosting is able to create highly accurate models that outperform traditional machine learning algorithms.
