This episode explores the k-nearest neighbors algorithm which is an unsupervised, non-parametric method that can be used for both classification and regression. The basica concept is that it leverages some distance function on your dataset to find the $k$ closests other observations of the dataset and averaging them to impute an unknown value or unlabelled datapoint.
Fler avsnitt av Data Skeptic
Visa alla avsnitt av Data SkepticData Skeptic med Kyle Polich finns tillgänglig på flera plattformar. Informationen på denna sida kommer från offentliga podd-flöden.
