In machine learning, what does "iterative" refer to in the context of gradient descent?

In machine learning, the term "iterative" in the context of gradient descent refers to the process of continuously updating the model parameters in small steps to minimize the loss function. This is achieved by calculating the gradient of the loss function with respect to the model parameters, which indicates the direction of the steepest increase in the loss. By moving in the opposite direction of this gradient, the model parameters are adjusted toward the minimum loss.

The iterative nature of this process is essential because it allows the algorithm to refine the parameters gradually, progressively improving the model's accuracy. Each iteration involves evaluating the current state of the model, adjusting based on the computed gradients, and then repeating this process until the model converges to an optimal set of parameters, or until a predefined number of iterations is reached.

This iterative approach contrasts with a one-time model fitting method, where parameters would be adjusted only once without any further updates. It differs from sampling data points, which relates to data preparation, and from cross-validation technique, which is used for model evaluation rather than adjusting model parameters directly. Thus, the emphasis on repeated parameter adjustments is key to understanding gradient descent and its effectiveness in training machine learning models.