Heuristics Documentation

Heuristics contains learners for training heuristic and black-box models within the IAI ecosystem. This documentation includes:

• quick start guides for various problem types that contain demos of the available learners in action:
  • classification
  • regression
  • survival
• details of the various learners, including descriptions of the available parameters:
  • Random Forest learners
  • XGBoost learners
  • GLMNet learners
• the Heuristics API reference

Citing Heuristics

If you use these heuristic methods in your work, we kindly ask that you cite the Interpretable AI software modules. We also ask that you reference the original work that first introduced the relevant algorithm:

• Random Forests:

  @article{breiman2001random,
    title={Random forests},
    author={Breiman, Leo},
    journal={Machine learning},
    volume={45},
    number={1},
    pages={5--32},
    year={2001},
    publisher={Springer}
  }

• XGBoost:

  @inproceedings{chen2016xgboost,
    title={Xgboost: A scalable tree boosting system},
    author={Chen, Tianqi and Guestrin, Carlos},
    booktitle={Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining},
    pages={785--794},
    year={2016}
  }

• GLMNet:

  @article{,
    title = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {33},
    number = {1},
    pages = {1--22},
    url = {http://www.jstatsoft.org/v33/i01/},
  }