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:

    1. Hyperoptimization of the Parameter-space using bayesian approach.

    1. Determines the best scoring model(s) using k-fold cross validation.

    1. Evaluates best model on independent evaluation set.

    1. Fit model on entire input-data using the best model.

    1. Works for classification and regression

    1. Creating a super-hyperoptimized model by an ensemble of all individual optimized models.

    1. Return model, space and test/evaluation results.

    1. Makes insightful plots.

Content

Installation

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.

Indices and tables