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Why do we need to normalize data for KNN?
For classification algorithms like KNN, we measure the distances between pairs of samples and these distances are influenced by the measurement units also. To avoid this miss classification, we should normalize the feature variables.
Why is it a good idea to first scale the data?
Feature scaling is essential for machine learning algorithms that calculate distances between data. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.
Why do you need to scale data in KNN cross?
But in this explanation, I don’t understand why a larger range in one of the features affects the predictions. 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.
Why do you need to scale your data for the k-NN algorithm?
The idea of the kNN algorithm is to find a k-long list of samples that are close to a sample we want to classify. Therefore, the training phase is basically storing a training set, whereas while the prediction stage the algorithm looks for k-neighbours using that stored data. Why do you need to scale your data for the k-NN algorithm?
Why is scaling required in KNN and k-means?
The algorithm should not be biased towards variables with higher magnitude. To overcome this problem, we can bring down all the variables to the same scale. One of the most common technique to do so is normalization where we calculate the mean and standard deviation of the variable.
Why do we need to scale the data?
Why do we need to scale the data? All such distance based algorithms are affected by the scale of the variables. Consider your data has an age variable which tells about the age of a person in years and an income variable which tells the monthly income of the person in rupees: