yramon / LimeCounterfactual
Hybrid algorithm based on SEDC and LIME for computing Evidence Counterfactuals (LIME-Counterfactual): explaining the model predictions of any classifier using a minimal set of features, such that removing these features results in a predicted class change.
☆11Updated 4 years ago
Alternatives and similar repositories for LimeCounterfactual:
Users that are interested in LimeCounterfactual are comparing it to the libraries listed below
- Generalized Optimal Sparse Decision Trees☆62Updated last year
- Public home of pycorels, the python binding to CORELS☆77Updated 4 years ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Heuristic best-first algorithm for computing Evidence Counterfactuals (SEDC): explaining the model predictions of any classifier using a …☆15Updated 4 years ago
- Bayesian time series forecasting and decision analysis☆115Updated last year
- WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)☆160Updated 2 years ago
- Editing machine learning models to reflect human knowledge and values☆124Updated last year
- Bayesian Additive Regression Trees For Python☆223Updated last year
- List of python packages for causal inference☆17Updated 3 years ago
- An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model☆74Updated 4 years ago
- Learn Pyro through the M5 forecasting competition☆84Updated 4 years ago
- A Python package for building Bayesian models with TensorFlow or PyTorch☆173Updated 2 years ago
- Python package for Imputation Methods☆248Updated last year
- 💊 Comparing causality methods in a fair and just way.☆138Updated 5 years ago
- ⬛ Python Individual Conditional Expectation Plot Toolbox☆165Updated 4 years ago
- scikit-learn gradient-boosting-model interactions☆25Updated last year
- Create sparse and accurate risk scoring systems!☆36Updated 8 months ago
- Python Accumulated Local Effects package☆165Updated 2 years ago
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆286Updated last year
- Time should be taken seer-iously☆314Updated 2 years ago
- Python implementation of iterative-random-forests☆64Updated last year
- Learning Certifiably Optimal Rule Lists☆173Updated 3 years ago
- Feature selection package based on SHAP and target permutation, for pandas and Spark☆30Updated 3 years ago
- Multi-Objective Counterfactuals☆41Updated 2 years ago
- Fast implementation of Venn-ABERS probabilistic predictors☆72Updated last year
- An extension of CatBoost to probabilistic modelling☆143Updated last year
- Meaningful Local Explanation for Machine Learning Models☆41Updated 2 years ago
- ☆22Updated last year
- python tools to check recourse in linear classification☆75Updated 4 years ago
- Missing data amputation and exploration functions for Python☆67Updated 2 years ago