Dm Standardization — Free Data Mining Tutorial
Learn Dm Standardization in Data Mining with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Dm Standardization in Data Mining with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Dm Standardization in Data Mining
Standardization transforms data to have mean 0 and standard deviation 1, useful for algorithms that assume normally distributed data or use distance metrics.
The Z-score formula is (x - mean) / standard_deviation. This is different from normalization in that it doesn't bound values to [0, 1].
Standardization is preferred for algorithms like KNN, K-Means, SVM, and neural networks that are sensitive to feature scaling.
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