We can now create an efficient model using the techniques that were discussed in the previous chapters. Bayesian optimization goes a long way in finding optimal hyperparameters. This chapter provides an overview of the Optuna framework and discusses further the role of hyperparameter optimization in automated machine learning. We’ll use Optuna to create our own little AutoML script. And then we’ll explore the Tree-based Pipeline Optimization Tool (TPOT), an AutoML tool that uses genetic programming to optimize machine learning pipelines.
Optuna
Like Hyperopt discussed ...
