Subject archive for "model-development," page 5

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 interpretability. Insights are drawn from Finale Doshi-Velez’s talk, “A Roadmap for the Rigorous Science of Interpretability” as well as the paper, “Towards a Rigorous Science of Interpretable Machine Learning”. The paper was co-authored by Finale Doshi-Velez and Been Kim. Finale Doshi-Velez is an assistant professor of computer science at Harvard Paulson School of Engineering and Been Kim is a research scientist at Google Brain.

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 Product Manager + Data Scientist at Concur Labs. Casari is also the co-author of the book, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. The full video of the talk is available here and special thanks to Amanda for providing permission to Domino to excerpt the talk’s slides in this Domino Field Note.

Sivan Aldor-Noiman, VP of Data Science at Wellio (now part of The Kraft Heinz Company), presented “The Past/Present/Future + Myths of Data Science” at Domino. This blog post provides a few highlights from the interactive talk as well as the full video.

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” at Rev. Higgins covers multilabel classification, a few methods used for multiclass prediction, and existing toolkits. This blog post provides highlights, the video, and a transcript of the talk.

Over the past few years, we’ve seen a new community of data science leaders emerge.

Over the past few years, we’ve seen a new community of data science leaders emerge.