whitebox
CARLA
whitebox | CARLA | |
---|---|---|
12 | 2 | |
181 | 265 | |
0.6% | 1.1% | |
2.8 | 0.0 | |
10 months ago | 7 months ago | |
Python | Python | |
MIT License | MIT License |
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whitebox
- [P] We made an open source ML / data monitoring platform
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A machine learning monitoring platform
Check it out on GitHub: https://github.com/squaredev-io/whitebox And, read the docs here: https://squaredev-io.github.io/whitebox/
- A new ML Monitoring platform. Stars help a lot 🤟
- I'm doing some work on the SDK of Whitebox, a new ML monitoring platform. First time building an SQD for mass consumption in python so feedback is essential. Here is the PR link.
- Hello, I am creating an open source ML monitoring platform. It's still early work and feedback is much appreciated. There is also a planned MLFlow integration. What do you think?
- We just open-sourced Whitebox for machine learning models monitoring!!!
- We just open-sourced Whitebox for machine learning models monitoring....
- We just open-sourced Whitebox for AI models monitoring....
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
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