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Test Driven Machine Learning

Software engineers are familiar with test driven development, but are not familiar with the statistical testing required in machine learning. Machine learning specialists are familiar with testing during the model building phase when they withhold data for cross-validation or final testing, but they are unfamiliar with software engineering principles. While testing a learned model gives an idea how well it might perform on unseen data it is not sufficient for model deployment. Trying to learn from test driven development practices we are looking across the machine learning life cycle to understand where we need to test and how this can be done. The testing of data, for example, is essential as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment. Testing the decisions made by a deployed machine learning model is equally important to understand if it delivers the expected business value. 


Detlef Nauck

Chief Research Scientist for Data Science @BTGroup and Visiting Professor @bournemouthuni

  Detlef Nauck is Chief Research Scientist for Data Science with BT's Research and Innovation Division located at Adastral Park, Ipswich, UK. He is leading a group of international scientists working on research into Data Science, Machine Learning and AI. Detlef focuses on establishing best...

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