Gradient boosting machines (GBMs) are a powerful family of machine learning techniques that have achieved significant success in various applications. They are highly customizable, allowing for different loss functions to be used depending on the task. This tutorial provides an introduction to the methodology of gradient boosting, focusing on machine learning aspects. It covers the theoretical foundations, practical examples, and considerations for model design, including handling model complexity and interpretation. The article begins by discussing the challenge of building non-parametric regression or classification models from data. It explains that traditional methods often rely on theoretical models, which may not be available in real-world scenarios. Instead, non-parametric techniques like neural networks or support vector machines can be used to build models directly from data. The most common approach is to build a single strong predictive model, but ensemble methods, which combine multiple weak models, are often more effective. Boosting methods, such as random forests and neural network ensembles, are discussed, highlighting their reliance on combining many weak models. Gradient boosting, a key technique in this family, is based on sequentially adding models to the ensemble, each trained to correct the errors of the previous ones. This approach is grounded in a gradient descent formulation, leading to the development of gradient boosting machines (GBMs). The methodology section describes the function estimation process, numerical optimization, and optimization in function space. It explains how GBMs iteratively fit new models to improve predictions, using loss functions that can be tailored to specific tasks. The gradient boosting algorithm is detailed, showing how new base-learner models are selected to be most correlated with the negative gradient of the loss function. The article then discusses the design of GBMs, including different loss functions for continuous and categorical responses, and various base-learner models such as decision trees and splines. It highlights the flexibility of GBMs in handling different types of data and the importance of choosing appropriate loss functions and base-learners for specific tasks. Regularization techniques, such as subsampling, shrinkage, and early stopping, are presented to prevent overfitting and improve model generalization. These methods help balance model complexity and predictive performance, ensuring that the model performs well on unseen data. Model interpretation is also addressed, emphasizing the importance of understanding how GBMs make predictions. Additive GBMs, for example, can be easily interpreted due to their additive components, while decision tree-based models require more careful analysis. Overall, the article provides a comprehensive overview of gradient boosting machines, covering their theoretical foundations, practical applications, and considerations for effective model design and interpretation.Gradient boosting machines (GBMs) are a powerful family of machine learning techniques that have achieved significant success in various applications. They are highly customizable, allowing for different loss functions to be used depending on the task. This tutorial provides an introduction to the methodology of gradient boosting, focusing on machine learning aspects. It covers the theoretical foundations, practical examples, and considerations for model design, including handling model complexity and interpretation. The article begins by discussing the challenge of building non-parametric regression or classification models from data. It explains that traditional methods often rely on theoretical models, which may not be available in real-world scenarios. Instead, non-parametric techniques like neural networks or support vector machines can be used to build models directly from data. The most common approach is to build a single strong predictive model, but ensemble methods, which combine multiple weak models, are often more effective. Boosting methods, such as random forests and neural network ensembles, are discussed, highlighting their reliance on combining many weak models. Gradient boosting, a key technique in this family, is based on sequentially adding models to the ensemble, each trained to correct the errors of the previous ones. This approach is grounded in a gradient descent formulation, leading to the development of gradient boosting machines (GBMs). The methodology section describes the function estimation process, numerical optimization, and optimization in function space. It explains how GBMs iteratively fit new models to improve predictions, using loss functions that can be tailored to specific tasks. The gradient boosting algorithm is detailed, showing how new base-learner models are selected to be most correlated with the negative gradient of the loss function. The article then discusses the design of GBMs, including different loss functions for continuous and categorical responses, and various base-learner models such as decision trees and splines. It highlights the flexibility of GBMs in handling different types of data and the importance of choosing appropriate loss functions and base-learners for specific tasks. Regularization techniques, such as subsampling, shrinkage, and early stopping, are presented to prevent overfitting and improve model generalization. These methods help balance model complexity and predictive performance, ensuring that the model performs well on unseen data. Model interpretation is also addressed, emphasizing the importance of understanding how GBMs make predictions. Additive GBMs, for example, can be easily interpreted due to their additive components, while decision tree-based models require more careful analysis. Overall, the article provides a comprehensive overview of gradient boosting machines, covering their theoretical foundations, practical applications, and considerations for effective model design and interpretation.