What is the purpose of model validation?

The purpose of model validation is primarily to evaluate a model's performance on a separate validation data set. This process is crucial in the machine learning life cycle as it helps determine how well the model generalizes to unseen data, which is not part of the training dataset. By using a separate validation set, practitioners can assess various performance metrics, such as accuracy, precision, recall, and F1 score, providing an unbiased estimate of the model’s ability to perform in real-world scenarios.

Model validation plays a significant role in identifying issues like overfitting, where a model performs well on training data but poorly on new data. It allows practitioners to gauge the effectiveness of the model in a controlled setting before deploying it in production.

In this context, training the model on new data is not the purpose of validation, as that is part of the training process. Adjusting hyperparameters for optimization falls under model tuning, which is different from validation. Classifying data into multiple categories relates to the application of a model post-validation rather than the validation process itself. Thus, evaluating a model's performance on a validation set is critical for a reliable and robust model development process.