xgboostExplainer
Model interpreter
Provides tools to understand and interpret the decisions made by XGBoost models in machine learning
An R package that makes xgboost models fully interpretable
253 stars
22 watching
68 forks
Language: R
last commit: over 6 years ago Related projects:
Repository | Description | Stars |
---|---|---|
scicloj/scicloj.ml.xgboost | Provides XGBoost models for machine learning tasks in Clojure | 7 |
lantanacamara/lightgbmexplainer | An R package to provide interpretability features for LightGBM models. | 23 |
h2oai/mli-resources | Provides tools and techniques for interpreting machine learning models | 483 |
mayer79/flashlight | A toolset for understanding and interpreting complex machine learning models | 22 |
jianbo-lab/l2x | A Python framework for learning to interpret models using information-theoretic methods | 123 |
pbiecek/xaiaterum2020 | An R package and workshop materials for explaining machine learning models using explainable AI techniques | 52 |
marcelrobeer/explabox | An exploratory tool for analyzing and understanding machine learning models | 14 |
leesael/edit | An implementation of a method to interpret ensemble models by learning compact representations from them | 8 |
interpretml/dice | Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
datamllab/xdeep | Provides tools for interpreting deep neural networks | 42 |
modeloriented/dalex | A tool to help understand and explain the behavior of complex machine learning models | 1,390 |
giuseppec/iml | Provides methods to interpret and explain the behavior of machine learning models | 494 |
ethicalml/xai | An eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance | 1,135 |
rmarko/explainprediction | An R package for explaining the predictions made by machine learning models in data science applications. | 2 |
modeloriented/modelstudio | A tool for creating interactive, model-agnostic explanations of machine learning models in R | 328 |