pyMGBoost is nothing more than a common interface for four popular gradient-boosting (ensemble) frameworks: XGBoost, CatBoost, LightGBM and scikitlearn Gradient Boosting. Meta (from the Greek μετά, meta, meaning "after" or "beyond") is a prefix meaning "more comprehensive" or "transcending." Similarly, the name was here used to mean that there are more than one gradient boosting implementation available. metaGBoost is shortened to MGB.
Gradient Boosting is a form og ensemble that has become the de-facto and almost ubiquitous solution for tabular data. Thus, I thought that having a small wrapper around it to get myself off the ground quickly was a good project. So here it is the reason of why this reposotiry was created.
- What is it? CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.
- Main page can be accessed here
- What is it?
- Main page can be accessed here
- What is it?
- Main page can be accessed here
- What is it?
- Main page can be accessed here
Given a dataset and some user-dependent parametres run the different frameworks and compare them. Essentially automate the following bullet point:
- Build a default model
- Build a user-defined model
- Tune the model
- Get the learning curves
- Get the model uncertainty
- Get the metrics
- Build an average/blended version of the models. Average is here intended as the arithmetic mean, whereas blended is here here a model where the user provides the weights to each model.
- Regression - currently under development
- Classification - supported in the future
The source code is split in several modules. I am aware that this may not the best options but it offer the most flexible way to code and test different options. I am open to suggestion on how to do it better from a sofware stand point.
- MGB/MetaGradientBoosting.py
- MGB/Modules.py
- XGBoostWrapper.py