Great demonstration. But classical statistics have tools specific to these sorts of data (binary logistic regression). Wouldn't it be appropriate to compare that to ML classification results? Mean comparisons between groups hardly capture the capabilities of classical statistics.
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Ooh that's a lovely, clear and simple example. Going to remember that one. Thanks for sharing!
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Is there a good citation that explores this in detail? would be nice to reference a paper that explains why multivariate classification accuracy can be good even when you don't have statistically significant univariate differences
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Forgive me if I am missing out on something important, I have zero training on ML.
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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that is a neat example
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Neuroscientist & academic at University of Cambridge 
may be statistically indistinguishable (p=0.1) if you compare their weights or height, but multivariable approach (height&weight) has >90% classification accuracy. This is an essential lesson for psychiatric neuroimaging!