Bonsai: Gradient Boosted Trees + Bayesian Optimization
Bonsai
Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.
Despite being a very small package, it has access to nearly all of the configurable parameters in XGBoost and CatBoost as well as the BayesianOptimization package allowing users to specify unique objectives, metrics, parameter search ranges, and search policies. This is made possible thanks to the strong similarities between both libraries.
$ pip install bonsai-tree
References/Dependencies:
Why use Bonsai?
Grid search and random search are the most commonly used algorithms for exploring the hyperparameter space for a wide range of machine learning models. While effective for optimizing over low dimensional hyperparameter spaces (ex: few regularization terms), these methods do not scale well to models