hgboost’s documentation!
The Hyperoptimized Gradient Boosting library (hgboost
), is a Python package for hyperparameter optimization for XGBoost, LightBoost, and CatBoost. HGBoost will carefully split the dataset into a train, test, and an independent validation set. Within the train-test set there is the inner loop for optimizing the hyperparameters using Bayesian optimization (based on Hyperopt) and, the outer loop is to test how well the best-performing models can generalize using an external k-fold cross validation. This approach will select the most robust model with the highest performance.

hgboost
is fun because:
It consists three of the most popular decision tree algorithms; XGBoost, LightBoost and Catboost.
It consists the most popular hyperparameter optimization library for Bayesian Optimization; Hyperopt.
An automated manner to split the data set into a train-test and independent validation to reliably determine the model performance.
The pipeline has a nested scheme with an inner loop for hyperparameter optimization and an outer loop with k-fold crossvalidation to determine the most robust and best-performing model.
It can handle both classification and regression tasks.
It is easy to go wild and create a multi-class model or an ensemble of boosted decision tree models.
It takes care of unbalanced datasets.
It aims to create explainable results for the hyperparameter search-space, and model performance results by creating insightful plots.
It is open-source.
It is documented with many examples.
You contribution is important
If you ❤️ this project, star this repo at the github page and have a look at the sponser page!
Github
Please report bugs, issues and feature extensions at github.
Content
Background
Installation
Background
Examples