I am currently studying knn algorithm and I want to know that why it is necessary to normalize all the variable in knn
For classification algorithms like KNN, we measure the distances between pairs of samples and these distances are influenced by the measurement units also. For example: Let’s say, we are applying KNN on a data set having 3 features.First feature ranging from 1-10, second from 1-20 and the last one ranging from 1-1000. In this case, most of the clusters will be generated based on the last feature as the difference between 1 to 10 and 1-20 are smaller as compared to 1-1000. To avoid this miss classification, we should normalize the feature variables.
Any algorithm where distance play a vital role for prediction or classification, we should normalize the variable as we do the same process in PCA also.
Hope this helps!