hgboost’s documentation!
hgboost
is short for Hyperoptimized Gradient Boosting and is a python package for hyperparameter optimization for xgboost, catboost and lightboost using cross-validation, and evaluating the results on an independent validation set.
hgboost
can be applied for classification and regression tasks.
hgboost
is fun because:
Hyperoptimization of the Parameter-space using bayesian approach.
Determines the best scoring model(s) using k-fold cross validation.
Evaluates best model on independent evaluation set.
Fit model on entire input-data using the best model.
Works for classification and regression
Creating a super-hyperoptimized model by an ensemble of all individual optimized models.
Return model, space and test/evaluation results.
Makes insightful plots.

Sponsor
This library is created and maintained in my free time. I like to work on my open-source libraries, and you can help by becoming a sponsor! The easiest way is by simply following me on medium, and it will cost you nothing! Simply go to my medium profile and press “follow”. Read more on my sponsor github page why this is important. This also gives you various other ways to sponsor me!
Star is important too!
If you like this project, star this repo at the github page! This is important because only then I know how much you like it :)
Quick install
pip install hgboost
Github
Github hgboost. Please report bugs, issues and feature extensions there.
Citing hgboost
The bibtex can be found in the right side menu at the github page.
Content
Background
Installation
Examples