In machine learning, what is the term for the process of improving model accuracy through iterative training?

The process of improving model accuracy through iterative training is known as training. During this phase, a machine learning algorithm learns from the input data, adjusting its parameters to minimize the error in predictions. This iterative process involves using the training dataset multiple times, where the model continuously refines its weights and biases to enhance its performance.

Training is fundamental in machine learning, as the quality of the learned model directly depends on how well it is trained. By processing the data in iterations, the algorithm can capture complex patterns and relationships within the dataset. This ongoing adjustment helps the model become more accurate over time as it learns from both successful and incorrect predictions.

Other terms mentioned in the options serve different purposes in the machine learning workflow. Validation refers to assessing the performance of the model on a separate subset of data to ensure that it generalizes well beyond the training dataset. Regularization involves techniques used to prevent overfitting by adding a penalty to the loss function, which helps the model maintain simplicity. Cross-validation is a method employed to evaluate the model's performance more reliably by dividing the dataset into multiple subsets, allowing for multiple training and testing cycles.