kernel regression

Kernel regression is a non-parametric method used for estimating the conditional expectation of a random variable. It involves fitting a local regression model at each point in the dataset using a kernel function to weight the contribution of nearby points. The kernel function determines the degree of influence that each point has on the estimate at a given point. The resulting estimate is a smooth curve that captures the underlying relationship between the input and output variables. Kernel regression is often used in machine learning and statistical modeling applications, and it is particularly useful when the relationship between variables is non-linear or when there is noise in the data.