Gradient Boosting

Gradient Boosting is a machine learning technique for regression and classification problems that builds a model in a stage-wise fashion.

Gradient Boosting is used in various fields such as finance, healthcare, and marketing to improve the accuracy of predictive models. It works by sequentially adding weak learners, typically decision trees, to the model. Each new tree corrects the errors made by the previous trees. The algorithm minimizes a loss function by using gradient descent, which adjusts the model to reduce prediction errors.

For example, in a regression problem, the first tree might predict the initial values. The second tree then predicts the residuals (errors) of the first tree, and so on. Each subsequent tree focuses on correcting the mistakes of the combined ensemble of previous trees. This process continues until the model reaches a specified number of trees or the error is minimized.

AdaBoost (Adaptive Boosting) is a variant of boosting algorithms. It adjusts the weights of incorrectly predicted instances, so subsequent learners focus more on difficult cases. While Gradient Boosting uses gradient descent to minimize the loss, AdaBoost changes the distribution of the training data to improve accuracy.

In summary, Gradient Boosting is a powerful ensemble technique that improves model performance by combining multiple weak learners. It is important to know because it provides high accuracy and flexibility for various predictive modeling tasks.