Matthew Conlen explains KDE well with a dynamic visualization , worth referring to.
Origin
KDE is a very important concept. It’s particularly useful when the true underlying distribution of data is uncertain and likely doesn’t follow a standard distribution (like a normal distribution).
Let’s use the data from Gaussian Mixture Distribution as an example:
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