After building some machine learning models in Python, R and Matlab, I found the latter's Classification Learner App to be immensely powerful. In the time it took me to build a single model in Python or R, I churned out 250 in Matlab.

Why don't auto-generated models features dominate when it comes to model creation? Also, why doesn't Matlab dominate as a result?

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    $\begingroup$ Were the 250 Matlab models more accurate in prediction than the single R or Python model? $\endgroup$ Jul 9, 2018 at 13:26
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    $\begingroup$ In addition, are you familiar with caret or scikt-lrn ? $\endgroup$
    – meh
    Jul 9, 2018 at 13:31
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    $\begingroup$ So what? Producing absolutely random predictions would be even faster. Ease of use, or speed are not the primary criteria for judging statistical software. $\endgroup$
    – Tim
    Jul 9, 2018 at 13:39
  • $\begingroup$ @aginensky, I hadn't come across caret. Thanks for sharing this one. But yes, I see your points that easily produced models may not offer better results than a single, well optimized model. It's something I hadn't considered. Thank you! $\endgroup$ Jul 10, 2018 at 15:04
  • $\begingroup$ I'm hearing the question as "what are the technical, production, or business weaknesses in this approach+solution in light of your limited (welcome to the human race) experience and exposure, that can cause it not to be the holy-grail (or silver-bullet or whatever). That doesn't sound so opinion based, and I think that stats folks could speak to it. They already are in the comments. Licensing fees. Alternative packages. $\endgroup$ Jul 10, 2018 at 15:16

1 Answer 1


Probably because of the high fees of MATLAB distributions, while R and Python are free.

  • $\begingroup$ I edited the question after you provided this answer, which was 'Why doesn't Matlab dominate the machine learning industry?' It was originally deemed an opinion-based question. $\endgroup$ Jul 10, 2018 at 15:08

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