Why do you need to scale data in KNN Could someone please explain to me why you need to normalize data when using K nearest neighbors. 
I've tried to look this up, but I still can't seem to understand it.
I found the following link:
https://discuss.analyticsvidhya.com/t/why-it-is-necessary-to-normalize-in-knn/2715
But in this explanation, I don't understand why a larger range in one of the features affects the predictions.
 A: The k-nearest neighbor algorithm relies on majority voting based on class membership of 'k' nearest samples for a given test point. The nearness of samples is typically based on Euclidean distance. 
Consider a simple two class classification problem, where a Class 1 sample is chosen (black) along with it's 10-nearest neighbors (filled green). In the first figure, data is not normalized, whereas in the second one it is. 


Notice, how without normalization, all the nearest neighbors are aligned in the direction of the axis with the smaller range, i.e. $x_1$ leading to incorrect classification. 
Normalization solves this problem!
A: Suppose you had a dataset (m "examples" by n "features") and all but one feature dimension had values strictly between 0 and 1, while a single feature dimension had values that range from -1000000 to 1000000. When taking the euclidean distance between pairs of "examples", the values of the feature dimensions that range between 0 and 1 may become uninformative and the algorithm would essentially rely on the single dimension whose values are substantially larger. Just work out some example euclidean distance calculations and you can understand how the scale affects the nearest neighbor computation.
A: If the scale of features is very different then normalization is required. This is because the distance calculation done in KNN uses feature values.
When the one feature values are large than other, that feature will dominate the distance hence the outcome of the KNN.
see example on gist.github.com
A: The larger the scale a particular feature has relative to other features, the more weight that feature will have in distance calculations.
Scaling all features to a common scale gives each feature an equal weight in distance calculations.
But notice that scaling introduces a particular weighting on the distance function, so how can we assume that it is somehow the correct one for KNN?
So my answer is: scaling should not be assumed to be a requirement.
