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In unsupervised learning, the scaling of the features has a great influence on the result. If a feature has a variance that is many times greater, it can dominate the target function of the algorithm. Therefore, it is of great importance to scale the input data in a way that their variability matches or at least does not contradict the semantics. There are ...


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Standardisation There are many reasons why we'd want to stardardise the data. The two most common ones are: In the cases where we want to apply an algorithm for which it would make sense to standardise. For instance, say you have a dataset where each row is a person and for each person you have two columns: The weight in kgs and the Height in meters. You ...


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If I understand correctly, you are trying to get a mean score for each vendor and you have multiple raters (people) who rate the vendors, but their scores are unreliable (because they consistently underrate or overrate a vendor). In that case one option would be to z-score (standardization) the raters' scores (not the vendors'), such that all scores for ...


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