Gradient Boosting for Imbalanced Data: Advanced Techniques and Solutions

1.背景介绍

Gradient boosting is a popular machine learning technique that has been widely used in various fields, such as computer vision, natural language processing, and data mining. It is an ensemble learning method that builds a strong classifier by combining multiple weak classifiers. The basic idea is to iteratively fit a new weak classifier to the residuals of the previous classifier, which helps to reduce the bias and improve the accuracy of the model.

In recent years, gradient boosting has been extensively studied and applied to imbalanced data, which is a common problem in many real-world applications. Imbalanced data refers to the situation where the distribution of classes in the dataset is highly skewed, leading to a biased model that performs