How does gradient boosting work in improving model accuracy?

Gradient boosting, also known as ensemble learning in machine learning, is a powerful technique that combines the strengths of several weak learners (typically decision trees) to improve model accuracy. Gradient boosting is a powerful ensemble learning technique that combines the strengths of multiple weak learners, typically decision trees. This technique builds up models in a sequence where each model is trained to forecast the residuals of the previous model rather than the target variables themselves. The overall model gets more accurate each time. https://www.sevenmentor.co...

Gradient boosting relies on the concept of the weak learner, a model which performs slightly above random chance. Weak learners are often decision trees, particularly shallow ones. This is due to the ease of interpretation and their ability to capture nonlinear patterns. In gradient boosting the first model predicts, and then the residuals (the difference between the predictions and actual target values) are calculated. These residuals are the errors that the model must fix. The residuals are then used to train a new model that predicts the errors. The process is repeated many times and each model attempts to reduce errors caused by the ensemble of previous models.