GAMA: Genetic Automated Machine learning Assistant
Pieter Gijsbers, Joaquin Vanschoren · The Journal of Open Source Software · 2019
Successful machine learning applications hinge on a plethora of design decisions, which require extensive experience and relentless empirical evaluation.To train a successful model, one has to decide which algorithms to use, how to preprocess the data, and how to tune any hyperparameters that influence the final model.Automating this process of algorithm selection and hyperparameter optimization in the context of machine learning is often called AutoML (Automated Machine Learning).