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.
Content
Background
Installation
Methods
Examples
Documentation
Quick install
pip install hgboost
Quick install
pip install hgboost
Source code and issue tracker
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.
Sponsor this project
If you like this project, star this repo and become a sponsor! Read more why this is important on my sponsor page!