What is the difference between feature scaling and normalization?
Feature scaling refers to techniques that adjust the range of features, while normalization specifically rescales data to a range of [0, 1].
Can you give me examples of feature scaling methods?
Sure! Common methods include Min-Max scaling, Standardization (Z-score), and Robust scaling.
When should I use normalization instead of feature scaling?
Normalization is best when you need to ensure that all features contribute equally, especially for algorithms sensitive to the scale.
Are there any drawbacks to using normalization?
Yes, normalization can be sensitive to outliers, which may skew the results if not handled properly.
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