Summary
In this chapter, you explored the most-used approaches in optimizing a specific model to perform well against a dataset and how you can even automate the process of model selection. You started by performing parallelized hyperparameter tuning using the HyperDriveConfig class to optimize the alpha parameter of the LassoLars model you have been training against the diabetes dataset. Then, you automated the model selection, using AutoML to detect the best combination of algorithms and parameters that predicts the target column of the diabetes dataset.
In the next chapter, you will build on top of this knowledge, learning how to use the AzureML SDK to interpret the model results.
