D. Why is it important to distinguish between outputs and outcomes when evaluating machine learning models?

1. It helps in understanding the computational complexity of the model.

2. It assists in evaluating the model's effectiveness in achieving the desired real-world impact.

3. It helps in optimizing the model's hyperparameters.

4. It assists in selecting the most appropriate machine learning algorithm.