meelgroup / MLIC
A new framework to generate interpretable classification rules
☆17Updated 2 years ago
Alternatives and similar repositories for MLIC:
Users that are interested in MLIC are comparing it to the libraries listed below
- Using Bayesian inference to mine rule sets☆10Updated 5 years ago
- Probabilistic Itemset Mining☆19Updated 8 years ago
- repository for R library "sbrlmod"☆25Updated 10 months ago
- Python Interface of the Scalable Bayesian Rule Lists☆19Updated 5 years ago
- Bayesian or-of-and☆34Updated 3 years ago
- The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).☆58Updated last year
- python tools to check recourse in linear classification☆75Updated 4 years ago
- ☕ A Python library for gradient-boosted statistical relational models / learning probabilistic relational programs.☆31Updated 2 years ago
- CAIPI turns LIMEs into trust!☆12Updated 4 years ago
- ☆16Updated 2 years ago
- Probabilistic Sequence Mining☆45Updated 6 years ago
- Empirical Likelihood for Contextual Bandits☆12Updated 4 years ago
- Python library for declarative, constrained, structured-output prediction.☆21Updated last year
- Source code for the ACML 2019 paper "Functional Isolation Forest".☆21Updated 2 years ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- LEMON: Explainable Entity Matching☆18Updated 2 years ago
- An implementation of IDS (Interpretable Decision Sets) algorithm.☆24Updated 4 years ago
- BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.☆32Updated last year
- Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…☆24Updated 2 years ago
- ☆20Updated 6 years ago
- Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.☆51Updated 11 months ago
- The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".☆23Updated last year
- Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"☆27Updated 4 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Random Forest model using Hellinger Distance as split criterion☆33Updated 2 years ago
- Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms☆14Updated 2 years ago
- https://arxiv.org/abs/2009.01561☆22Updated 2 years ago
- Multi-Objective Counterfactuals☆41Updated 2 years ago
- Performance estimation for time series forecasting tasks☆38Updated 4 years ago