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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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Accelerating model velocity through Snowflake Java UDF integration

Domino Data Lab

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. anomaly detection).

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

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Unlocking the Power of Better Data Science Workflows

Smart Data Collective

Phase 4: Knowledge Discovery. Finally, models are developed to explain the data. Algorithms can also be tested to come up with ideal outcomes and possibilities. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner.

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Knowledge Graphs and Healthcare

Ontotext

They also developed a large-scale knowledge graph for an early hypothesis testing tool. This is where experience counts and Ontotext has a proven methodology for semantic data modeling that normalizes both data schema and instances to concepts from major ontologies and vocabularies used by the industry sector. Tried and Tested.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

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Designing a SemTech Proof-of-Concept: Get Ready for Our Next Live Online Training

Ontotext

The training is structured to follow the steps of building a simple prototype to test the feasibility of the technology with hands-on guidance by experienced instructors. The most important question our training tries to answer, both in theory and in practice, is how to approach a use case that is a good fit for semantic technology.