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26 Jul 2025
Building machine learning models is just one part of the journey... Understanding them is the real challenge. Shapash is a Python library that brings transparency to machine learning by making model predictions understandable for everyone technical or not. 🛠️ Key Features of Shapash: - Generates a WebApp for navigating global and local explainability - Makes predictions easily interpretable with clear visualizations and labels - Supports regression, binary, and multiclass classification - Compatible with CatBoost, XGBoost, LightGBM, Scikit-learn, Linear Models, SVMs - Helps summarize and export local explanations - Offers metrics to evaluate the quality of explanations - Allows filtering subsets (correct/wrong predictions, feature values) for deeper analysis - Can be deployed via API or in batch mode for production use 🔍 Whether you're working on risk models, customer churn, or trading strategies if your ML model makes decisions, Shapash can help you explain why. GitHub: github.com/MAIF/shapash?tab=… Worth checking out if interpretability matters in your work. Are you someone who wants to apply AI in trading? Curious how GenAI, LLMs, and machine learning are changing the trading landscape? Then this conference is for YOU. 🎯 QuantInsti’s Algorithmic Trading Conference 2025 📅 Date: 23 September, 2025 🕒 Time: 6:00 PM IST | 8:30 PM SGT | 8:30 AM EDT 💻 Free | Online | Global What’s happening? Workshop by Tucker Balch (Emory University) Explore real-world use of AI, LLMs & price data in trading strategies. See how AI models are predicting inter-stock relationships, with live Q&A! Topics include: - How AI is transforming trading desks - Emerging skills for quants - GenAI's role in quant education - What the future of finance looks like with AI 👥 Who should attend? - Aspiring Quants - Traders & Finance Professionals - Coders & ML Engineers - Students & Career Switchers 🎟️ Ready to see how AI is shaping the future of trading? 🔗 Register now - spots are limited! 👉 quantinsti.com/algorithmic-t… #AlgoTrading #AIinTrading #QuantConference #QuantFinance #GenAI #LLM #FinanceCareers #QuantLearning #QuantInsti #EPAT #MachineLearning #ExplainableAI #MLInterpretability #XAI #Shapash #QuantFinance #AITransparency #PythonML #QuantInsti #MLTools
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26 Jun 2023
The latest ERCIM News special theme dives into Explainable AI (XAI) – uncovering its application across healthcare, industry, ethics, climate change, and generative language models. ercim-news.ercim.eu #XAI #AItransparency #MLinterpretability
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13 Jun 2021
How to #deploy and execute ML models as a service with the leading cloud provider like #AWS in a single click flexrule.com/archives/decisi… #AI #ML #IntelligentAutomation #ExplainableML #ExplainableAI #MLInterpretability #Productionize #Serverless #CloudComputing
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4 Jun 2021
Want to know how to #deploy and execute ML models as a service with the leading cloud provider like #GoogleCloud in a single click?flexrule.com/archives/decisi… #AI #ML #IntelligentAutomation #ExplainableML #ExplainableAI #MLInterpretability #Productionize #Serverless #CloudComputing
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3 Jun 2021
How to #deploy and execute ML models as a service with the leading cloud provider like #AWS in a single click flexrule.com/archives/decisi… #AI #ML #IntelligentAutomation #ExplainableML #ExplainableAI #MLInterpretability #Productionize #Serverless #CloudComputing
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¿Cómo la #AI y #ML facilitan el control de cebo en granjas porcinas mediante la plataforma #PigML? ¿Cómo mejorar la gestión de las explotaciones lácteas? #DataAnalytics #ML #DL #rstats #PredictiveModelling #clustering #IoT #BigData #MLinterpretability #AgriTech
20 Feb 2020
📝Conoce cómo hacer las #ExplotacionesGanaderas más eficientes a través de las #TIC. Nueva entrega en el #blog sobre #SmartFarming ➡️ ow.ly/qVCf50yrcR7 #IA #MachineLearning #VideoAnalytics #IoT #BigData
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This New Google Technique Help Us Understand How Neural Networks are Thinking Thanks to ⁦@TDataScience#ML #AI #DL #MachineLearning #ArtificialIntelligence #DeepLearning #Interpretability #MLInterpretability towardsdatascience.com/this-…

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Interactively browsing #ShapleyValues with #PartialDependence and a surrogate decision tree. To learn how to build those custom views for #MLInterpretability join me at #ODSC_India for the #KNIME #GuidedAnalytics #Learnathon!
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