tudelft-cda-lab / GROOT
[ICML 2021] A fast algorithm for fitting robust decision trees. http://proceedings.mlr.press/v139/vos21a.html
☆22Updated last year
Alternatives and similar repositories for GROOT:
Users that are interested in GROOT are comparing it to the libraries listed below
- [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples☆67Updated 2 years ago
- Class Prior Estimation in Active Positive and Unlabeled Learning☆15Updated 4 years ago
- This project is a research on how to extract rules from the existing data using trained Decision Tree. The dataset used to train the mode…☆16Updated 5 years ago
- Optimal Sparse Decision Trees☆103Updated 2 years ago
- A package for tree-based statistical estimation and inference using optimal decision trees.☆40Updated 9 months ago
- An algorithm for learning optimal decision trees, with Python interface☆64Updated last year
- Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018.☆39Updated 3 years ago
- Gradient boosted decision trees for multiple outputs. Better generalization ability, faster training and inference.☆45Updated 3 months ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Transfer algorithms on Decision Trees☆17Updated 4 years ago
- OCEAN: Optimal Counterfactual Explanations in Tree Ensembles (ICML 2021)☆20Updated last year
- Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the …☆65Updated last year
- ☆13Updated 3 years ago
- A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules☆21Updated last year
- Python Implementation of Bertsimas's "Optimal classification trees".☆33Updated 4 years ago
- Versatile Verification of Tree Ensembles☆17Updated 10 months ago
- TransBoost algorithm for transfer learning☆35Updated 2 years ago
- A collection of algorithms of counterfactual explanations.☆50Updated 4 years ago
- Implementation of the paper End-to-end Learning of Deterministic Decision Trees☆17Updated 2 years ago
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox☆44Updated last week
- Random Forest or XGBoost? It is Time to Explore LCE☆66Updated last year
- This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) .☆103Updated 6 years ago
- The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".☆24Updated last year
- Python Interface of the Scalable Bayesian Rule Lists☆19Updated 5 years ago
- Python Meta-Feature Extractor package.☆133Updated 10 months ago
- TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks☆69Updated 3 weeks ago
- Python wrapper for LibRec and other recommendation frameworks.☆29Updated last year
- Measuring data importance over ML pipelines using the Shapley value.☆38Updated 2 months ago
- Bandit based Reinforcement Learning applied on Feature selection, a Monte Carlo search tree algorithm is trained to find the best feature…☆21Updated 3 years ago
- Generalized Optimal Sparse Decision Trees☆63Updated last year