i know statsmodel library in Python and in R, lowess and loess functions are available for this but i have a few problems with them:
1- i can't seem to be able to make predictions on new data for either
2- it doesn't seem to support a feature space grater than 1
is there anyway to implement Locally Weighted Linear Regression without these problems? (preferably in Python)
UPDATE: according to @xavier-bourret-sicotte's answer i used Alexandre Gramfort's implementation for this but still it doesn't seem that i can predict on new unseen data (test set)? is there anyway to implement this in the context of sklearn so we can use predict method?