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    Themes and Conferences per Pacoid, Episode 9

    Paco Nathan's latest article features several emerging threads adjacent to model interpretability. Introduction Welcome back to our monthly burst of themes and conferences....

    SHAP and LIME Python Libraries: Part 2 - Using SHAP and LIME

    This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers...

    Item Response Theory in R for Survey Analysis

    In this guest blog post, Derrick Higgins, of American Family Insurance, covers item response theory (IRT) and how data scientists can apply it within...

    Classify All the Things (with Multiple Labels)

    Derrick Higgins of American Family Insurance presented a talk, “Classify all the Things (with multiple labels): The most common type of modeling task...

    Data Science Models Build on Each Other

    Alex Leeds, presented “Building Up Local Models of Customers” at a Domino Data Science Popup. Leeds discussed how the Squarespace data science team...

    Using Bayesian Methods to Clean Up Human Labels

    Derrick Higgins, AmFam Data Science & Analytics, discusses how Bayesian methods can be applied to improve the quality of annotated training sets. ...

    What Your CIO Needs to Know about Data Science

    What would you rather be doing? Data science or DevOps? As a data scientist, your CIO may hear from you that model deployment is a challenge (e.g.,...

    Humans in the Loop

    This guest blog post from Paco Nathan dives into how people and machines collaborating together to perform work is real and not science...

    Model Deployment Powered by Kubernetes

    In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background ...