[EN] Test Driven Data Analysis - with Nick Radcliffe
Code for Thought
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The Importance of Interpretation in Machine Learning
I think interpretation is the hardest part. And I think it's the part we've got least far with developing. But it is super important. It's almost like part of the Hippocratic oath for data scientists ought to be, we're not going to say it's your fault if you interpret my beautiful output wrongly. So there's lots that we can do by hand. We can label things clearly. We can write out sentences. We can highlight things. We can have. You'll see on a lot of graphs these days, people actually write, more is better.
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