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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139
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Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Adrian Weller (2017-07-29). “

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Digital listening reveals 3 leading innovation drivers

CIO Business Intelligence

higher [in 2022] than in 2017.” The inherent capabilities of AI–to process vast amounts of data and use learned intelligence to make decisions with extraordinary speed–enable opportunities uncovered through digital listening. AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E.

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Some highlights from 2020

Data Science and Beyond

I’ve been working remotely with Automattic since 2017, so I was pretty covid-ready as far as work was concerned. The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Remote work. Only time will tell. Sustainability.

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Strengthening cybersecurity in life sciences with IBM and AWS

IBM Big Data Hub

Leading life science companies are leveraging cloud for innovation around operational, revenue and business models. In 2017, 94% of hospitals used electronic clinical data from their EHR. AWS Shared Responsibility Model When it comes to security, AWS follows a Shared Responsibility Model between the customer and AWS.

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What Is Data Intelligence?

Alation

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. HBR Review May/June 2017.