Hey thanks for the comment! I'm the author of the talk-turned-post :-) You are completely correct about reproducibility being super important in data science workflows. While I did mention it in the post (and in the talk the post was based on), I mentioned it as a part of version control tools. That said, I think it's not something that is focused on enough (obviously I'm guilty of that too) so I plan on doing a follow up post focused on reproducibility and the tools that can help you recreate your results. Kinda putting the "science" back in data science. Really I want an excuse to play around with tools like https://dataversioncontrol.com/ which looks super useful and I mentioned it in the post, but haven't had a chance to use.
Hi! I'm Stephanie Kim and wrote the article. This post and slides were from a talk I gave for a basic introduction to machine learning at a woman's programming conference in Seattle. I did update the language which was a mistake rather than a misunderstanding of Naive Bayes. I have professional machine learning experience and while I am definitely not an expert the talk was geared for web developers with no prior experience in machine learning. Thanks for your feedback.
Hi, I'm Stephanie Kim and wrote the talk/post. Thanks for the comment! Yes you are correct I should have specified that it is the features of each input rather than the inputs that are regarded as independent from one another! I will revise that in the post. Again, thanks for pointing that out since it's an important distinction, especially for people just starting out!