Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

Gradient boosting is indeed best defined as an ensemble learning technique. This method works by combining multiple weak learners, typically decision trees, to create a strong predictive model. The process involves training these learners sequentially, with each new learner focusing on the errors made by the previous ones.

Specifically, gradient boosting takes a gradient descent approach to minimize a loss function, which optimizes the model's predictions. As each tree is added, its predictions are adjusted based on the gradient of the loss function, allowing the ensemble to improve accuracy incrementally. This sequential correction is a hallmark of gradient boosting and differentiates it from other ensemble methods such as bagging, where models are trained independently.

While option A touches on the aspect of combining models to enhance accuracy, it does not capture the specific mechanics of how gradient boosting operates within the context of ensemble learning. The focus on weak learners and optimization through gradient descent distinguishes gradient boosting as a unique method within the broader category of ensemble techniques. The reasoning behind the other options, such as reducing overfitting or improving linear regression, doesn't relate directly to the specific nature of gradient boosting, which primarily emphasizes the sequential learning process and adjustment based on previous errors.