What does "gradient descent" refer to?

Gradient descent is an optimization algorithm that is fundamental to many machine learning models. Its primary purpose is to minimize the loss function, which quantifies how well a model is performing based on predictions compared to actual outcomes. The algorithm works by iteratively adjusting the model parameters—such as weights—based on the gradients (or derivatives) of the loss function with respect to those parameters. This process helps find the minimum point of the loss function, enabling the model to improve its predictions over time.

In the context of machine learning, this iterative approach is crucial because it guides the adjustments needed to reduce errors in predictions, thus helping to train models effectively. By updating parameters in the direction of the steepest descent (the negative gradient), the algorithm systematically hones in on the optimal set of parameters that yield the most accurate predictions. This is why the first option accurately defines gradient descent.