Gradient boosting

What is gradient boosting?

Gradient boosting is used as a machine learning technique to predict future values or trends based on historical data. It can be employed for time series forecasting, where the goal is to make predictions about future values of a variable based on its past observations.

Specifically, it is an ensemble learning method that combines the predictions of multiple weak learners to create a strong learner that can make more accurate predictions. The main idea behind gradient boosting is to build the weak learners sequentially, each one correcting the errors made by its predecessor.

Where does it derive from?

The term “gradient” refers to the use of gradient optimization techniques, like gradient descent, to find the best weights or parameters for each weak learner during the iterative process. Gradient boosting is known for its high performance, flexibility, and ability to handle complex datasets. However, as with any machine learning algorithm, it’s essential to tune hyperparameters carefully to prevent overfitting and achieve optimal results.