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...
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...
On the Importance of Community-Led Open Source
Wes McKinney, Director of Ursa Labs and creator of pandas project, presented the keynote, "Advancing Data Science Through Open Source" at Rev. McKinney's...
Model Management and the Era of the Model-Driven Business
Over the past few years, we’ve seen a new community of data science leaders emerge. Regardless of their industry, we have heard three...
Code
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 Scientist? Programmer? Are They Mutually Exclusive?
This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”....
Machine Learning
On Ingesting Kate Crawford’s “The Trouble with Bias”
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May...
The Machine Learning Reproducibility Crisis
Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of...
Practical Techniques
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...
Building a Domino Web App with Dash
Randi R. Ludwig, Data Scientist at Dell EMC and an organizer of Women in Data Science ATX, covers how to build a Domino...
Leaders at Work
Data Science Use Cases
In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is...
Measuring Data Science Business Value
This blog post covers metrics that help data science leaders ensure their team’s work is aligned to business value. Data science managers and...
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...
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...
Put Models at the Core of Business Processes
At Rev, Nick Elprin, Domino's CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He...
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...