Automated Machine Learning in Action

6 AutoML with a fully customized search space

This chapter covers

• Customizing the entire AutoML search space without connecting AutoML blocks
• Tuning autoencoder models for unsupervised learning tasks
• Tuning shallow models with preprocessing pipelines
• Controlling the AutoML process by customizing tuners
• Joint tuning and selection among deep learning and shallow models
• Hyperparameter tuning beyond Keras and scikit-learn models

This chapter introduces customization of the entire AutoML search space in a layerwise fashion without wiring up AutoML blocks, giving you more flexibility in designing the search space for tuning unsupervised learning models and optimization algorithms. We introduce how to tune a shallow model with its preprocessing pipeline, ...