I am not familiar with ML background. Thus have this simple question. Suppose I have training data for my dependent variable which depends on say 3 independent variables. Suppose I use some regression method and perform a fit according to that model on my training data.
Suppose now I pass independent variables from testing data and see the predicted value of my dependent variable.
Now, my question is, when I perform this non-parametric fit, what type of function gets stored or what actually happens such now when I supply some testing data, it predicts almost actual value.
Like, if I say, I perform a polynomial fit. I know that it is a line y = mx + c something and thus on giving x, I can predict y. And here the value of m and c gets derived from the training data. So now what type of function or parameters are involved in non-parametric fit?
Also, if I use scikit-learn, is there a method to see the values of these parameters derived from training data fit?