Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

The optimization process in machine learning primarily aims to minimize the loss function. The loss function quantifies how well the model's predictions align with the actual outcomes in the training data. By minimizing this function, the learning algorithm seeks to adjust its parameters, thereby enhancing the model's performance—essentially reducing the difference between predicted and actual values.

When the loss function is minimized, it indicates that the model has likely learned the underlying patterns in the training data more effectively. This process often involves employing optimization algorithms like gradient descent, which iteratively update the model parameters to find the values that yield the lowest possible loss.

The other options, while relevant in various contexts of model training, do not directly relate to the optimization goal. For instance, minimizing the number of features or modifying the training dataset size may be strategies for improving model complexity or handling overfitting, but they are not the primary focus of the optimization process itself. The model parameters are indeed what gets adjusted during optimization, but the optimization's target is the loss function that evaluates the model's quality.