Training an AutoML regression model

Compared to setting up your Jupyter environment and preparing your data, training an AutoML model involves fewer steps. First, you will need to set a name for your experiment. Remember that experiments automatically log information about your AutoML runs. Next, you will need to set your Target column, which is the column you wish to predict, and a few other settings. Finally, you will use AutoML to train a model and watch the results in real time.

In this section, you will create an experiment, configure the various parameters and settings specific to AutoML regression tasks, and train three AutoML regression models using the datasets you created in the previous section. Let's get started:

1. Set Experiment and give it a name by using the following code. This is where all of the logs and metrics of your run will be stored in the AML studio:

   experiment_name = 'Diabetes-Sample-Regression'
   exp = Experiment(workspace=ws, name...