moDel Agnostic Language for Exploration and eXplanation
☆1,459Jan 20, 2026Updated 2 months ago
Alternatives and similar repositories for DALEX
Users that are interested in DALEX are comparing it to the libraries listed below
Sorting:
- DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanatio…☆690Feb 21, 2023Updated 3 years ago
- 📍 Interactive Studio for Explanatory Model Analysis☆332Aug 31, 2023Updated 2 years ago
- iml: interpretable machine learning R package☆501Feb 24, 2025Updated last year
- Extensions for the DALEX package☆67Jan 14, 2026Updated 2 months ago
- modelDown generates a website with HTML summaries for predictive models☆120Jul 8, 2022Updated 3 years ago
- Explanatory Model Analysis. Explore, Explain and Examine Predictive Models☆198Apr 14, 2024Updated last year
- Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)☆84Nov 30, 2023Updated 2 years ago
- Variable Importance Plots (VIPs)☆190Dec 12, 2025Updated 3 months ago
- Model verification, validation, and error analysis☆59Jan 9, 2024Updated 2 years ago
- Fit interpretable models. Explain blackbox machine learning.☆6,816Updated this week
- An R wrapper of SHAP python library☆58May 25, 2023Updated 2 years ago
- Extra recipes for predictor embeddings☆144Jan 29, 2026Updated last month
- Model Agnostics breakDown plots☆104Mar 12, 2024Updated 2 years ago
- Algorithms for explaining machine learning models☆2,617Oct 17, 2025Updated 5 months ago
- Local Interpretable Model-Agnostic Explanations (R port of original Python package)☆492Dec 11, 2025Updated 3 months ago
- Documentation for the DALEX project☆36Feb 28, 2024Updated 2 years ago
- Flexible tool for bias detection, visualization, and mitigation☆85Oct 31, 2025Updated 4 months ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,501Jul 13, 2025Updated 8 months ago
- Effects and Importances of Model Ingredients☆38Mar 15, 2023Updated 3 years ago
- Interpretability and explainability of data and machine learning models☆1,768Feb 26, 2025Updated last year
- H2O.ai Machine Learning Interpretability Resources☆491Dec 12, 2020Updated 5 years ago
- Data generator for Arena - interactive XAI dashboard☆31Sep 30, 2020Updated 5 years ago
- Fast approximate Shapley values in R☆132May 24, 2025Updated 9 months ago
- An R-focused pipeline toolkit for reproducibility and high-performance computing☆1,341Dec 4, 2024Updated last year
- A set of tools to understand what is happening inside a Random Forest☆239Mar 25, 2024Updated last year
- Pipeable steps for feature engineering and data preprocessing to prepare for modeling☆612Jan 5, 2026Updated 2 months ago
- A game theoretic approach to explain the output of any machine learning model.☆25,131Mar 12, 2026Updated last week
- A tidy unified interface to models☆647Mar 12, 2026Updated last week
- A curated list of awesome responsible machine learning resources.☆3,998Updated this week
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,573Feb 24, 2026Updated 3 weeks ago
- Visual analysis and diagnostic tools to facilitate machine learning model selection.☆4,395Feb 19, 2025Updated last year
- vtreat is a data frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. Distr…☆286Jan 9, 2025Updated last year
- Classes and functions to create and summarize resampling objects☆340Jan 30, 2026Updated last month
- A library for debugging/inspecting machine learning classifiers and explaining their predictions☆2,775Feb 10, 2026Updated last month
- Machine Learning in R☆1,679Aug 22, 2025Updated 6 months ago
- Surrogate Assisted Feature Extraction in R☆28Aug 13, 2022Updated 3 years ago
- Lime: Explaining the predictions of any machine learning classifier☆12,105Jul 25, 2024Updated last year
- Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME met…☆35Aug 21, 2019Updated 6 years ago
- 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models☆3,154Feb 6, 2026Updated last month