- [Q] What's the community's opinion of "interpretable ML/AI"? https://github.com/slundberg/shap 11 comments statistics
- A unified approach to explain the output of any machine learning model. https://github.com/slundberg/shap 9 comments learnmachinelearning
Linking pages
- Didact AI: The anatomy of an ML-powered stock picking engine · Principia Mundi https://principiamundi.com/posts/didact-anatomy/ 103 comments
- Can a Machine Learning Model Predict the SP500 by Looking at Candlesticks? | Mario Filho | Machine Learning http://mariofilho.com/can-machine-learning-model-predict-the-sp500-by-looking-at-candlesticks/ 33 comments
- GitHub - zziz/pwc: This repository is no longer maintained. https://github.com/zziz/pwc 13 comments
- GitHub - microsoft/LightGBM: A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. https://github.com/Microsoft/LightGBM 11 comments
- Building a Better Search Engine for Semantic Scholar | by Sergey Feldman | AI2 Blog https://medium.com/ai2-blog/building-a-better-search-engine-for-semantic-scholar-ea23a0b661e7 8 comments
- GitHub - maziarraissi/Applied-Deep-Learning: Applied Deep Learning Course https://github.com/maziarraissi/Applied-Deep-Learning 6 comments
- The Pragmatic Data Scientist. 5 Insights into commercial Machine… | by Jens Møllerhøj | Medium https://medium.com/@mollerhoj/the-pragmatic-data-scientist-f5d24404805f 4 comments
- GitHub - xplainable/xplainable: Real-time explainable machine learning for business optimisation https://github.com/xplainable/xplainable 4 comments
- GitHub - benedekrozemberczki/awesome-decision-tree-papers: A collection of research papers on decision, classification and regression trees with implementations. https://github.com/benedekrozemberczki/awesome-decision-tree-papers 3 comments
- GitHub - Mybridge/amazing-machine-learning-opensource-2019: Amazing Machine Learning Open Source Tools and Projects for the Past Year (v.2019) https://github.com/Mybridge/amazing-machine-learning-opensource-2019 3 comments
- Going beyond simple error analysis of ML systems https://alexandruburlacu.github.io/posts/2021-07-26-ml-error-analysis 2 comments
- 2018’s Top 7 Libraries and Packages for Data Science and AI: Python & R | by Favio Vázquez | Heartbeat https://heartbeat.fritz.ai/top-7-libraries-and-packages-of-the-year-for-data-science-and-ai-python-r-6b7cca2bf000 2 comments
- A deep-learning algorithm to classify skin lesions from mpox virus infection | Nature Medicine https://doi.org/10.1038/s41591-023-02225-7 2 comments
- Deep Learning vs. Machine Learning: What Are the Differences? | Apriorit https://www.apriorit.com/dev-blog/472-machine-learning-applications 1 comment
- GitHub - stefan-jansen/machine-learning-for-trading: Code for Machine Learning for Algorithmic Trading, 2nd edition. https://github.com/stefan-jansen/machine-learning-for-trading 1 comment
- Artificial Intelligence: a concise conceptual introduction | Towards Data Science https://medium.com/@leandromineti/artificial-intelligence-d1e45efc99b4 1 comment
- How to build TRUST in Machine Learning, the sane way | by Eyal Trabelsi | Bigabid’s Brain | Medium https://medium.com/bigabids-dataverse/how-to-build-trust-in-machine-learning-the-sane-way-39d879f22e69 0 comments
- GitHub - jphall663/awesome-machine-learning-interpretability: A curated list of awesome machine learning interpretability resources. https://github.com/jphall663/awesome-machine-learning-interpretability 0 comments
- Feature importance — what’s in a name? | by Sven Stringer | bigdatarepublic | Medium https://medium.com/bigdatarepublic/feature-importance-whats-in-a-name-79532e59eea3 0 comments
- GitHub - EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning https://github.com/EthicalML/awesome-production-machine-learning 0 comments
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