Model selection and evaluation are crucial steps in the machine learning pipeline. It involves choosing the best model for a given task, tuning hyperparameters, and assessing the model’s performance. In this blog post, we will explore several aspects of model selection and evaluation, including cross-validation, hyperparameter tuning, model persistence, validation curves, and learning curves. 1.…