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I am quite new to advanced statistical methods and concepts but i would like to know a few things about KDE. I understand that KDEs are used to estimate the probability density function of a series of data values. But looking at the snapshot above, could it also be used as a classification method based on certain characteristics of a data or object. The picture below is from a dataset of 3 different species of the same flower with 4 attributes measured: Petal length, Petal Width, Sepal Length and Sepal Width. Judging from the corresponding KDE plots is it safe to assume that Petal Length is the best discriminant between the 3 species? followed closely by Petal Width? if not, what is the best way to extract information from this data using KDE?

Folarin
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  • There is more information in the bivariate plots in so far as the univariate plots are marginal reductions (or representations) of the bivariate plots and so lose information. – Nick Cox Dec 05 '17 at 15:27
  • "the same flower" means the same genus; for more on this canonical dataset see e.g. https://stats.stackexchange.com/questions/74776/what-aspects-of-the-iris-data-set-make-it-so-successful-as-an-example-teaching – Nick Cox Dec 05 '17 at 15:29

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