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How To Make Data-Driven Predictions with Predictive Modeling

When you hear words like machine learning (ML) or artificial intelligence (AI), one of the first things that comes to mind is correctly predicting future occurrences or...

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot...

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Justified Algorithmic Forgiveness?

Last week, Paco Nathan referenced Julia Angwin’s recent Strata keynote that covered algorithmic bias. This Domino Data Science Field Note dives a bit...

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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...

Data Science Code Model Management Practical Techniques Predictive Models R Item Response Theory Generative Models IRT Models Model Development

Data Science is more than Machine Learning

This Domino Data Science Field Note provides highlights and video clips from Addhyan Pandey’s Domino Data Pop-Up talk, “Leveraging Data Science in...

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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 ...

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Benchmarking Predictive Models

It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our...

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Reflections on "Buy vs Build" for Data Science Tools

“Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections...

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