Because height and weight are correlated you need to do the KDE with a bivariate Gaussian Kernel.Hello, I have to make a project where I have 2 given classes of data (males, females) which are described by 2 variables (height, weight). What I have to do is build a bayesian classifier which uses Parzen windows (kernel density estimator) to classify the data.
What I did is applied the kernel density estimator first for females described by height, then I applied it for females described by weight and I multiplied these results, thinking it would show the probability of being a female described by height and weight (but I guess I was wrong). For me, it is not clear what probability is the result of the kernel density estimation. I don’t really know how to apply the bayes classifier next because I don’t understand what probability (prior,conditional, etc) I get as the result of kernel density estimator.
The formula I used is the one in the definition here Kernel density estimation - Wikipedia, the free encyclopedia , where the kernel is the gaussian function.
I hope my point is understood, as English is not my first language.
What do you think?