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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 deeper into some...

Themes and Conferences per Pacoid, Episode 2

Paco Nathan's column covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. Introduction Welcome back to...

Trust in LIME: Yes, No, Maybe So? 

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that may help data...

Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, writes about benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST....

Themes and Conferences per Pacoid, Episode 1

Introduction: New Monthly Series! Welcome to a new monthly series! I’ll summarize highlights from recent industry conferences, new open source projects, interesting research, great examples, amazing...

Make Machine Learning Interpretability More Rigorous

This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a rigorous evaluation of...

Learn from the Reproducibility Crisis in Science

Key highlights from Clare Gollnick’s talk, “The limits of inference: what data scientists can learn from the reproducibility crisis in science”, are covered in this Domino...

Feature Engineering: A Framework and Techniques 

This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao Paulo. Casari is the Principal...

Three Simple Worrying Stats Problems

In this guest post, Sean Owen, writes about three data situations that provide ambiguous results and how causation helps clarifies the interpretation of data. A version...

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 no one talks about”...

Avoiding a Data Science Hype Bubble

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, advocates for a common taxonomy of terms within the data science industry. The proposed...

Model Evaluation

This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation metrics for supervised learning models and offline...

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 built models to...

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