… what is it?
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Weighted local polynomial regression is a way to predict what will happen in future based on data from the past. It uses data from the past to fit a curve or line to the data, but it also takes into account how close the data points are to each other, by assigning weights to each of the data points. The closer the data points are, the higher the weight given to the data point. This helps the prediction be more accurate.
Simply put, it is to use information it already has to complete something. This is used in some AIs like DALL-E that expand an image. It uses the data it’s already got and tries to predict what is, or could, be in the outer bounds of the image.
Weighted local polynomial regression is a type of regression analysis that weights the regression equation based on the proximity of neighboring points to the point being estimated. This technique is particularly useful when the data have a non-linear relationship or when there are outliers in the data set. It works by fitting a polynomial to the data points that are closest to the point being estimated, and then weighting the equation by the distance of each point from the point being estimated. This helps to smooth out the data and improve the accuracy of the regression equation.
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