ML-tuning - model selection and hyperparameter tuning

Model development is one of the major tasks. However an important ML task is the selection of the best model from among a list of models, and tuning the model for optimal performance. Tuning can obviously be done for the individual steps or the entire pipeline model, which would include multiple algorithms, feature engineering, transformations and selections.

MLLib supports model selection using the following tools:

• Cross Validator
• Train Validation Split

We will look at Model Tuning in Chapter 9, Building a Recommendation System, on Recommendations to see how we can minimize mean squared error, one of the characteristics of a good model.