KDE

Kernel Density Estimation (KDE) is a statistical technique used to create a smooth, continuous curve that represents the distribution of a set of data points. Imagine plotting all data points on a graph; KDE takes each point and spreads its influence smoothly around it, like a gentle hill. When multiple points are close together, their hills overlap and create a higher peak. Combining all these overlapping hills results in a smooth curve that shows where data is most concentrated, helping you understand the overall pattern or shape of the data without relying on rigid bin categories.