What is primarily adjusted during hyperparameter tuning?

The correct choice refers to hyperparameter tuning as the process of adjusting non-learnable parameters of a model that influence its performance. Hyperparameters are settings that govern the training process, model architecture, or learning procedure but are not learned from the training data itself, unlike model weights, which are optimized during the training phase.

Tuning hyperparameters is essential because it can significantly affect how well a model performs on a given task. For instance, adjusting the learning rate can prevent a model from converging too quickly or oscillating, while modifying the regularization parameters can help prevent overfitting.

The other choices do not accurately describe what is adjusted during hyperparameter tuning. The weights assigned to different features refer to the model's learned parameters, which are optimized during training, rather than during the hyperparameter tuning process. The training data size and type are determined prior to the model training and are typically not altered during hyperparameter tuning. The architecture of the machine learning model can be designed or selected before tuning but is not usually adjusted in the context of hyperparameter optimization; rather, hyperparameters within a model's existing architecture are fine-tuned.