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ArangoML Part 3: Bootstrapping and Bias-Variance

Estimated reading time: 2 minutes

This post is the third in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

  • Introduce bootstrapping and bias-variance concepts
  • Estimate and analyze the variance of the model from part 2
  • Capture the metadata for this activity with arangopipe
ArangoML Pipeline Cloud

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

These posts will hopefully appeal to two audiences:

  • The first half of each post is for beginners in machine learning
  • The second half for those already using machine learning

We decided to do it this way to provide a jumping-off point for those interested in machine learning while still showing useful examples for those who already have a machine learning pipeline.

Check it out on github

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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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