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ArangoML Part 4: Detecting Covariate Shift in Datasets

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This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more. 

In this post we:

  • Introduce the concept of covariate shift in datasets
  • Showcase the built-in dataset shift detection API
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning

Posts in this series:
ArangoML Part 1: Where Graphs and Machine Learning Meet
ArangoML Part 2: Basic Arangopipe Workflow
ArangoML Part 3: Bootstrapping and Bias Variance
ArangoML Part 4: Detecting Covariate Shift in Datasets
ArangoML Series: Intro to NetworkX Adapter
ArangoML Series: Multi-Model Collaboration

Check it out on github

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Chris

Christopher Woodward

Chris has over 10 years experience at all angles of technology including service, support, and development. He is also passionate about learning and right now he is focused on improving the learning experience for the ArangoDB community. Chris believes the future is native multi-model and wants to help tell the world.

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