FAQ: K-Nearest Neighbor Regressor - Weighted Regression

This community-built FAQ covers the “Weighted Regression” exercise from the lesson “K-Nearest Neighbor Regressor”.

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This exercise can be found in the following Codecademy content:

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FAQs on the exercise Weighted Regression

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Why is the denominator the sum of 1/each distance?

I think it’s something like the weighted average but inversed. I checked several webpages saying that it’s called “Inversed Distance Weighting”. I will use some pseudo-code to explain.

Imaging you’re doing a weighted average for a list of number and their counts, the weighted average should equal to
numbers = [the list of numbers]
counts = [the list of corresponding counts]
weighted_average = sum([number * count for (number, count) in zip(numbers, counts)] / sum(counts)

You could imagine sum(counts) as
sum = 0
for count in counts:
sum += count

Now we want to do a inversed weighted average, which means we could convert each count to 1/count. The larger count, the smaller 1/count.

Thus, the denominator becomes 1/count_1 + 1/count_2 + 1/count_3 + ... 1/count_n

That’s how I understand the inversed weighted average! Hope it could help you. :grinning: