Gradient boosting

Gradient boosting is an ensemble machine learning technique used to create predictive models. Gradient boosting builds models in a stage-wise fashion by working from small decision trees. Each successive tree tries to augment the overall predictive power of all previously trained decision trees.

The main idea of gradient boosting is to train predictors sequentially with the aim of minimizing an overall objective function. Gradient boosting uses a technique called boosting which works by combining weak learners into a strong one. Weak learners are those which are only slightly better than random guessing. The idea is to combine multiple weak learners into a single, more powerful learner, using boosting techniques.

In the boosting process, first of all, a base or weak learner is identified. For each subsequent step, the boosting technique weighs or assigns different weights to individual weak learners, so that they become more effective individually and collectively as a whole. By the end of the boosting process, a single learner is produced which has much more predictive power than just the combination of the weak learners.

Other notable features of gradient boosting include its flexibility in using many different types of weak learners, its capacity to handle non-linear relationships between input variables and target, and its efficiency in dealing with large datasets.

Gradient boosting has been used in a variety of applications due to its great predictive power and efficiency. In particular, it has been successfully employed in areas such as fraud detection, risk assessment, recommendation systems, computer vision, and artificial intelligence. Compared to other machine learning techniques, gradient boosting can be seen as particularly useful in imbalanced datasets.