Which steps are completed by AutoML in the machine learning workflow?

The correct choice encompasses several critical steps within the machine learning workflow that AutoML typically automates. AutoML is designed to streamline and enhance the process of developing machine learning models, making it more accessible and efficient, especially for users who may not have extensive expertise in the field.

In this context, data preparation refers to tasks such as cleaning and preprocessing the dataset, handling missing values, and feature engineering, which are essential before any modeling can occur. After preparing the data, AutoML automates model training, where it explores various algorithms and hyperparameter settings to find the best-performing model for the given problem.

Following model training, evaluation is conducted, where the performance of the model is assessed using metrics relevant to the task, such as accuracy, precision, recall, or F1 score, depending on whether the task is classification or regression. Finally, result display is part of the process, allowing users to visualize the performance metrics and understand model predictions through interpretative dashboards or visualizations generated by the AutoML system.

The option that suggests only data visualization and reporting is incomplete, as it neglects the critical phases of data preparation, model training, and evaluation. Manual configuration of algorithms is contrary to the goals of AutoML, which seeks to reduce the need