linkedin / TE2RulesLinks
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
☆62Updated last year
Alternatives and similar repositories for TE2Rules
Users that are interested in TE2Rules are comparing it to the libraries listed below
Sorting:
- Probabilistic Gradient Boosting Machines☆157Updated last year
- Helpers for scikit learn☆16Updated 2 years ago
- ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any…☆102Updated 3 years ago
- Surrogate Assisted Feature Extraction☆37Updated 4 years ago
- An extension of CatBoost to probabilistic modelling☆147Updated 2 years ago
- A power-full Shapley feature selection method.☆211Updated 2 months ago
- An automated machine learning tool aimed to facilitate AutoML research.☆102Updated last year
- Example usage of scikit-hts☆57Updated 3 years ago
- stratx is a library for A Stratification Approach to Partial Dependence for Codependent Variables☆66Updated last year
- An unsupervised feature selection technique using supervised algorithms such as XGBoost☆90Updated last year
- ☆103Updated last week
- (ICLR 2024) GRANDE: Gradient-Based Decision Tree Ensembles☆95Updated 5 months ago
- 💊 Comparing causality methods in a fair and just way.☆141Updated 5 years ago
- CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system☆77Updated 2 years ago
- [AMAI 2024] Selective: Feature Selection Library☆69Updated 2 months ago
- Repository for the explanation method Calibrated Explanations (CE)☆70Updated this week
- hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating t…☆64Updated 9 months ago
- Fast and incremental explanations for online machine learning models. Works best with the river framework.☆55Updated 11 months ago
- Bayesian time series forecasting and decision analysis☆120Updated 2 years ago
- A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and …☆63Updated 4 years ago
- A Causal AI package for causal graphs.☆60Updated 2 weeks ago
- Fast implementation of Venn-ABERS probabilistic predictors☆75Updated last year
- Train Gradient Boosting models that are both high-performance *and* Fair!☆106Updated last week
- Editing machine learning models to reflect human knowledge and values☆127Updated 2 years ago
- Methods for online conformal prediction.☆117Updated 7 months ago
- Cyclic Boosting Machines - an explainable supervised machine learning algorithm☆62Updated last year
- A library for Time Series EDA (exploratory data analysis)☆72Updated last year
- TimeSHAP explains Recurrent Neural Network predictions.☆193Updated last year
- The stream-learn is an open-source Python library for difficult data stream analysis.☆66Updated 3 months ago
- WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)☆159Updated 3 years ago