Rforestry - Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability
Provides fast implementations of Honest Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation.
Last updated 22 days ago
openblascpp
5.06 score 1 dependents 76 scripts 432 downloadsdistillML - Model Distillation and Interpretability Methods for Machine Learning Models
Provides several methods for model distillation and interpretability for general black box machine learning models and treatment effect estimation methods. For details on the algorithms implemented, see <https://forestry-labs.github.io/distillML/index.html> Brian Cho, Theo F. Saarinen, Jasjeet S. Sekhon, Simon Walter.
Last updated 2 years ago
bartdistillation-modelexplainable-machine-learningexplainable-mlinterpretabilityinterpretable-machine-learningmachine-learningmodelrandom-forestxgboost
3.92 score 7 stars 12 scripts 257 downloads