Not much of that makes sense.
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I hope that everyone who retweeted or liked that piece thinks that too. His point #4 hints the reason for writing it: he doesn't get it.
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- Poisson regression is not a type of logistic regression, both are GLMs. - Binary data violates lots of the assumptions linear regression relies on, so could go badly wrong. - Logistic regression coefficients are easier to interpret than most other classifiers (eg SVMs).
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Should Data Scientists not capitalize on the huge knowledge base from epidemiology and statistics? This is an appalling paper.
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And you would think that even for
@DataScienceCtrl a person writing about logistic regression would have been required to spend a couple of hours studying the subject. This is truly appallingly bad quality of writing.
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This is by far not the worst page on data science central. My personal favorite:https://www.datasciencecentral.com/profiles/blogs/deep-learning-definition-resources-comparison-with-machine-learni …
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And also this particular gem: "Machine learning is (...) When these algorithms are automated, as in automated piloting or driver-less cars, it is called AI, and more specifically, deep learning" https://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A505006 …
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Ya know, the decision boundary is actually a straight line cause its a.......... linear model (contrary to its name). Soooooooooooo I guess you validated your own blog post since an expert like yourself seems to have confused the logistic function with logistic regression.
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IMHO this blog hardly makes any sense & adds to the confusion of beginners. People hoping to understand data science, try out Hopkins' online course taught by leading data scientists
@jtleek@rdpeng@bcaffohttps://www.coursera.org/learn/data-scientists-tools …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Čini se da učitavanje traje već neko vrijeme.
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