xgbimputer 0.1.0

Extreme Gradient Boosting imputer for Machine Learning.

XGBImputer

XGBImputer is an effort to implement the concepts of the MissForest algorithm proposed by Daniel J. Stekhoven and Peter Bühlmann[1] in 2012, but leveraging the robustness and predictive power of the XGBoost[2] algorithm released in 2014.

The package also aims to simplify the process of imputing categorical values in a scikit-learn[3] compatible way.

Installation

$ pip install xgbimputer

License

This project is licensed under the terms of the Apache-2 license.

References

• [1] Daniel J. Stekhoven and Peter Bühlmann. "MissForest—non-parametric missing value imputation for mixed-type data."

• [2] XGBoost

• [3] scikit-learn