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In sklearn.preprocessing.Normalizer it mentioned that “”Scaling inputs to unit norms is a common operation for text classification or clustering for instance. For instance the dot product of two l2-normalized TF-IDF vectors is the cosine similarity of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community””.

The things that I don’t get are

  1. Why Scaling inputs to unit norms is a common operation for text classification or clustering for instance?

  2. What purpose (Normalizer) does it solve in text classification or clustering problems?

  3. The example that is given…For instance, the dot product of two l2-normalized TF-IDF vectors is the cosine similarity of the vector…………. When we do dot product of two l2-normalized TF-IDF vectors? I mean in what context it is said?

  4. Is Clustering here means Un-supervised ML?

  5. Is sklearn.preprocessing.Normalizer only used in text classification or clustering? Or Are there any other situations where it is used?

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