What is the purpose of the AutoML iterations?

The purpose of the AutoML iterations is to train and tune multiple models. AutoML is designed to automate the process of applying machine learning to real-world problems. During the iterations, various algorithms and their hyperparameters are explored to find the best configuration for the specific dataset. This mechanism involves generating numerous models, evaluating their performance, and iteratively refining them based on metrics such as accuracy, precision, or recall.

By enabling the training of multiple models, AutoML can effectively search through various combinations of algorithms and hyperparameter settings, ensuring that it identifies a model that best fits the data. This comprehensive approach contrasts with the idea of creating a single optimal model, which limits the potential that could arise from experimenting with different methods and configurations. Additionally, while visualizing results and compiling training data are important aspects of machine learning workflows, they do not encapsulate the primary function of the AutoML iteration process. The primary focus remains on training and tuning to enhance model performance.