ML Zoomcamp 1.5 - Model Selection Process

The document discusses the process of selecting the best machine learning model, including evaluating multiple models on a holdout dataset to determine the best performing one. It notes common model types like logistic regression, decision trees, and neural networks that could be considered. It also addresses the need to split data into training, validation, and test sets to accurately assess model performance and avoid overfitting conclusions from multiple comparisons of models during selection.