KDE in seaborn distplot()

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What is the meaning of KDE? When we get a KDE after plotting the data how can we find whether it is correct or not?
Jul 16 in Python by Hiran
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1 answer to this question.

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Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis.

Setting the hist flag to False in distplot will yield the kernel density estimation plot.
answered Jul 16 by Kunal

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