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When we must separate the set of objects from the information, then we use discrimination. Then again, classification is all about classifying the info into predefined teams.
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Outlier refers to those info factors, which noticeably vary from the other observations. An outlier exists because of the measurement variability or When you've got any experimental mistake.
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