alibi
CARLA
| alibi | CARLA | |
|---|---|---|
| 4 | 2 | |
| 2,289 | 263 | |
| 0.6% | 0.4% | |
| 7.7 | 0.0 | |
| 8 days ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
alibi
- Alibi: Open-source Python lib for ML model inspection and interpretation
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
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Best alternatives to 'shap' package?
Alibi explain might be an option depending on what you are looking for https://github.com/SeldonIO/alibi
CARLA
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[R] CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Abstract: Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favourable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available open–source implementations, the decision in favour of certain models is primarily based on what is readily available. Going forward – to guarantee meaningful comparisons across explanation methods – we present CARLA (Counterfactual And Recourse Library), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open sourced CARLA and our experimental results on GitHub, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.
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University of Tübingen Researchers Open-Source ‘CARLA’, A Python Library for Benchmarking Counterfactual Explanation Methods Across Data Sets and Machine Learning Models
4 Min Read| Paper | Github
What are some alternatives?
interpret - Fit interpretable models. Explain blackbox machine learning.
carla - Open-source simulator for autonomous driving research.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
conductor - Conductor is a microservices orchestration engine.
rliable - [NeurIPS'21 Outstanding Paper] Library for reliable evaluation on RL and ML benchmarks, even with only a handful of seeds.
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
MindsDB - The platform for customizing AI from enterprise data
sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.