SeldonIO / alibiLinks
Algorithms for explaining machine learning models
☆2,540Updated last month
Alternatives and similar repositories for alibi
Users that are interested in alibi are comparing it to the libraries listed below
Sorting:
- XAI - An eXplainability toolbox for machine learning☆1,194Updated 3 years ago
- Interpretability and explainability of data and machine learning models☆1,714Updated 5 months ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,434Updated 3 weeks ago
- Algorithms for outlier, adversarial and drift detection☆2,415Updated 2 months ago
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,486Updated last month
- A library for debugging/inspecting machine learning classifiers and explaining their predictions☆2,772Updated 3 months ago
- Source code/webpage/demos for the What-If Tool☆966Updated 10 months ago
- python partial dependence plot toolbox☆860Updated 11 months ago
- Code for "High-Precision Model-Agnostic Explanations" paper☆805Updated 3 years ago
- moDel Agnostic Language for Exploration and eXplanation☆1,433Updated last week
- Feature engineering package with sklearn like functionality☆2,101Updated last month
- Bias Auditing & Fair ML Toolkit☆727Updated 2 months ago
- OmniXAI: A Library for eXplainable AI☆938Updated last year
- Fit interpretable models. Explain blackbox machine learning.☆6,630Updated last week
- A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitig…☆2,630Updated 7 months ago
- Natural Gradient Boosting for Probabilistic Prediction☆1,765Updated 3 weeks ago
- A Python package to assess and improve fairness of machine learning models.☆2,109Updated last week
- A model-agnostic visual debugging tool for machine learning☆1,669Updated 6 months ago
- A machine learning package for streaming data in Python. The other ancestor of River.☆781Updated last year
- 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models☆2,937Updated 2 weeks ago
- A curated list of awesome responsible machine learning resources.☆3,842Updated this week
- Extra blocks for scikit-learn pipelines.☆1,350Updated last month
- Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.☆2,416Updated last week
- Hummingbird compiles trained ML models into tensor computation for faster inference.☆3,456Updated 3 weeks ago
- Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are look…☆439Updated 11 months ago
- nannyml: post-deployment data science in python☆2,087Updated 3 weeks ago
- Interesting resources related to XAI (Explainable Artificial Intelligence)☆838Updated 3 years ago
- Model interpretability and understanding for PyTorch☆5,345Updated 2 weeks ago
- Leave One Feature Out Importance☆836Updated 5 months ago
- EvalML is an AutoML library written in python.☆821Updated this week