Question: Suppose we have a classification problem where the training data are tuples (yi , xi) with xi = [xi1, ..., xiD] R D. Suppose we

Suppose we have a classification problem where the training data are tuples (yi , xi) with xi = [xi1, ..., xiD] R D. Suppose we re-scale the feature vectors x i = [xi1/s1, ..., xiD/sD] using different scale factors sd > 0 for different dimensions d. Is the KNN classification function using standard Euclidean distance invariant to this re-scaling? That is, will it always be the case that fKNN (xi) = fKNN (x i )? Explain your

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