Running AutoML experiments with code

So far, in this chapter, you were fine-tuning a LassoLars model, performing a hyperparameter tuning process to identify the best value for the alpha parameter based on the training data. In this section, you will use AutoML in the AzureML SDK to automatically select the best combination of data preprocessing, model, and hyperparameter settings for your training dataset.

To configure an AutoML experiment through the AzureML SDK, you will need to configure an AutoMLConfig object. You will need to define the Task type, the Metric, the Training data, and the Compute budget you want to invest. The output of this process is a list of models from which you can select the best run and the best model associated with that run, as shown in Figure 9.11:

Figure 9.11 – AutoML process

Depending on the type of problem you are trying to model, you must select the task parameter, selecting either classification, regression, or...