The contribution of the weak learner to the ensemble is based on the gradient descent optimisation process.
Intuitively, gradient boosting is a stage-wise additive model that generates learners during the learning process (i.e., trees are added one at a time, and existing trees in the model are not changed). As gradient boosting is based on minimising a loss function, different types of loss functions can be used resulting in a flexible technique that can be applied to regression, multi-class classification, etc. Gradient descent is a first-order iterative optimisation algorithm for finding a local minimum of a differentiable function.
Gradient boosting re-defines boosting as a numerical optimisation problem where the objective is to minimise the loss function of the model by adding weak learners using gradient descent. We already know that gradient boosting is a boosting technique.Let us see how the term ‘gradient’ is related here. The term gradient boosting consists of two sub-terms, gradient and boosting. On completion, you will receive a Certificate from The University of Texas at Austin, and Great Lakes Executive Learning. This 12-month program offers a hands-on learning experience with top faculty and mentors. This course will help you learn from a top-ranking global school to build job-ready AIML skills. Check out Great Learning’s PG program in Artificial Intelligence and Machine Learning to upskill in the domain. While there are ample resources available online to help you understand the subject, there’s nothing quite like a certificate. The two main boosting algorithms are Adaptive Boosting(AdaBoost) and Gradient Boosting. Over the last years boosting techniques like AdaBoost and XGBoost have become much popular because of their great performance in online competitions like Kaggle. In boosting terminology, the simple models are called weak models or weak learners. With the introduction of more simple models, the overall model becomes a stronger predictor. Understand gradient boosting algorithm with exampleīoosting is loosely-defined as a strategy that combines multiple simple models into a single composite model.Difference between Gradient Boosting and Adaptive Boosting(AdaBoost).Gradient Boosting, as the name suggests is a boosting method. There are various ensemble methods such as stacking, blending, bagging and boosting.
Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.
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