Rather, nonparametric models determine the model structure from the underlying data. Unlike traditional estimation techniques, nonparametric estimation does not assume that data is drawn from a known distribution. Before diving too deeply into kernel density estimation, it is helpful to understand the concept of nonparametric estimation. Kernel density estimation is a nonparametric model used for estimating probability distributions. In this blog, we look into the foundation of KDE and demonstrate how to use it with a simple application. Kernel density estimation (KDE), is used to estimate the probability density of a data sample.
In today's blog, we examine a very useful data analytics tool, kernel density estimation.
