AutoML: Automated Machine Learning
Data evaluation and pre-processing: Data is prepared, cleansed, and transformed to create a useful model-training dataset.
Feature engineering: New columns of data are created in the existing model-training data, which may better represent predictors in the phenomenon described by the data or simply work better with the ML algorithms.
Feature selection: After new features are built, AutoML picks only those that are useful in generating a model.
Algorithm selection: Competing candidate models are reviewed to select the one that best performs in terms of desired metric (E.g., optimizing for accuracy, recall, balanced accuracy).
Hyperparameter tuning: A set of optimal hyperparameters is chosen for a learning algorithm.