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That's easy - statisticians take pride in models that are understandable, while machine learning practitioners take pride in models that are not.
by js8 7y ago
That's easy - statisticians take pride in models that are understandable, while machine learning practitioners take pride in models that are not.
- im3w1l 7y agoI'd change that to say machine learning practitioners take pride in performance. If the best method of predicting planetary motions is to model them as a hierarchy of triangular epicycles then that's exactly what a machine learning practitioner will do.
- js8 7y agoThat's kind of my point, that they have different tradeoffs, although I do not hide my preference for explainability (or model soundness). :-) The example you give reminds me of Chomsky and Norvig debate on the topic.
- ACow_Adonis 7y agoUnderstandable? Two words: statistical significance :P I've seen university employees who still don't understand what it is and can't explain it...and just about everyone who uses it gets it wrong...
- dr_dshiv 7y agoOn the +J+
- mschuetz 7y agoNot sure why you're downvoted. Also noticed that everyone uses statistics because you're expected to, but hardly anyone knows how to do it right.
- chalst 7y agoMost scientists who use statistics are not statisticians.
- bregma 7y ago95% of scientists who use statistics are not statisticians, plus or minus 5%, 19 times out of 20.
- dorgo 7y ago"statistical significance" - it has too many problems. A colleague complained once, that his test kept alternating between statistical significant and not significant back and forth. I know it's not how it is supposed to work, but most people using it don't know (or care) how to use it. My opinion: just don't use this concept at all.
- alexgmcm 7y agoIt reminds me of Dirac on Poetry: >In science one tries to tell people, in such a way as to be understood by everyone, something that no one ever knew before. But in poetry, it's the exact opposite.
- wodenokoto 7y agoI would take out the snark and say: statisticians take pride in models that are understandable, while machine learning practitioners take pride in models that have high accuracy.
- edejong 7y agoNo, accuracy is but one possible goal. "Machine learning practitioners take pride in models that optimize the value of that model." And since we cannot define exactly what this value is (accuracy, RMSE, bias or a specific confusion matrix), we have to make a more abstract definition. We are now left with a question. Is this abstract definition quantifiable? If so, we can still exclude the statistician w.r.t. understandably. However, if we allow qualitative value as well, the traditional statistician is back at the table. Now, a further refinement needs to be made. Since fairness, accountability and transparency in Data Science are in the limelight, we can make a point that our ability to understand is a key metric for Machine Learning models as well. It is interesting to see the tendency to associate words or phrases to key neurons in embeddings. For example, in embeddings of faces, we can associate gender, ethnicitity, fatness and gaze with some of the embedded neurons.
- bregma 7y agoStatisticians take pride in models that are understandable, while machine learning practitioners take pride in models that are profitable.
- mhermher 7y agoStatistics is science and ML is engineering.
- dorgo 7y agoI don't know. My former ML profs do a lot of science too...