Training an AutoML multiclass model
Multiclass classification involves predicting three or more classes instead of the standard binary classification. Using custom machine learning, training multiclass models is often a messy, complicated affair where you have to carefully consider the number of classes you are trying to predict, how unbalanced those classes are relative to each other, whether you should combine classes together, and how you should present your results. Luckily, AutoML takes care of all these considerations for you and makes training a multiclass model as simple as training a binary classification model.
In this section, you load in data using the publicly available Iris dataset. You will then set your AutoML classifications for multiclass classification, train and register a model, and examine your results. You will notice that much of the code is identical to the last section. By understanding the differences between binary and multiclass classification in AutoML...
