Split Decisions

Noah is part of a team building a Machine Learning model to control electric substations. The team recently attended a training session to learn how to prepare the data for model training.

Although the session covered the importance of splitting the data into train, validation, and test sets, it didn't explain why this is necessary.

Noah wants to understand the reasoning behind splitting the data.

What are the reasons for splitting a dataset before training a Machine Learning model?

To prevent underfitting of the model

To prevent overfitting of the model

To make the training process faster

To accurately evaluate the model's performance