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normalization
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Does k-means make assumptions? Its drawbacks explained
Why k-means, though 'just an optimization', behaves as if clusters were round, equally spread and equally sized, and what happens when they are not.
Read the answer →PCA, eigenvectors and eigenvalues explained in plain language
PCA finds the directions your data varies most. Eigenvectors are those directions; eigenvalues say how much variance each one carries.
Read the answer →Centering vs standardizing predictors in regression: when to do which?
Centering and standardizing never change how well a regression fits. They change what the coefficients mean. Here is when each one helps.
Read the answer →How do you normalize data to a 0-1 range?
Min-max scaling rescales any set of numbers so the smallest value becomes 0 and the largest becomes 1, using one formula.
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