Which algorithm is used to adjust model parameters during training to minimize the loss function?

The optimizer is the correct answer because it is specifically designed to adjust the parameters of a machine learning model during the training process. Its primary objective is to minimize the loss function, which measures how well the model's predictions align with the actual outcomes.

During training, the optimizer iteratively modifies the weights and biases of the model using various techniques, such as gradient descent. By calculating the gradient of the loss function, the optimizer determines the direction and magnitude of adjustments needed to reduce the loss, thereby improving the model's accuracy.

In contrast, a classifier is a type of model used for categorizing data into classes. A regressor is used for predicting continuous values rather than for minimizing loss directly. An evaluator assesses the performance of the model after training but does not engage in parameter adjustment itself. Thus, the optimizer plays a critical role in the training phase by ensuring that the model learns effectively from the data.