What is the term for the method that adjusts outputs to improve model performance by changing the loss function?

The term that describes the method of adjusting outputs to improve model performance by changing the loss function is known as the penalization mechanism. This approach involves modifying the loss function to incorporate penalties for undesirable behaviors or outputs, thereby encouraging the model to learn more effectively by avoiding these penalized behaviors. In various machine learning contexts, this adjustment helps refine the training process by penalizing certain errors or promoting desired characteristics in the model’s predictions.

Considering the other options, gradient checkpointing refers to a technique used to manage memory during model training by storing only a subset of gradients and recomputing others when needed. It does not directly change outputs or loss functions. Cross-entropy loss is a specific type of loss function often used for classification tasks but does not inherently involve penalization for errors outside its predetermined structure. The objective function is a broader term that encompasses the overall function being optimized in a model, which may include various types of loss functions and does not focus specifically on the mechanism of penalization.

In summary, the penalization mechanism specifically pertains to the strategic adjustment of the loss function to enhance model accuracy and performance.