Which of the following sentences is true about model evaluation and model validation in ML pipelines?

Model validation is the process of confirming that a machine learning model meets the specified requirements and will perform as expected on new, unseen data from the problem domain. It is a crucial set of tasks aimed at assessing the model's generalization capabilities and ensuring it is the "right" model for the intended application. This often involves techniques like cross-validation or using a hold-out validation set to tune hyperparameters and prevent overfitting, thereby confirming the model's fitness for purpose before final deployment.

A. Model evaluation and validation are distinct; evaluation typically refers to the final assessment on a test set.
B. Model evaluation is an integral part of the ML pipeline, used to measure performance with metrics.
D. While validation often precedes final evaluation, this describes sequence, not definition. C is the better, definitional statement.

1. Google. (n.d.). Machine Learning Crash Course: Training and Test Sets: Splitting Data. Retrieved from developers.google.com/machine-learning/crash-course/training-and-test-sets/splitting-data. This resource distinguishes the roles of the training set, validation set (for tuning), and test set (for final evaluation).

2. Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for validating classification models. Chemometrics and Intelligent Laboratory Systems, 177, 24-35. https://doi.org/10.1016/j.chemolab.2018.04.002. The paper discusses validation as a process to "assess how the results of a statistical analysis will generalize to an independent data set." (Abstract).

3. Stanford University. (n.d.). CS 329S: Machine Learning Systems Design. Course materials frequently discuss the importance of validation for ensuring a model performs as expected on data representative of the production environment.