Cosine Similarity

#machine-learning #statistics #interview

Cosine Similarity is used to calculate the similarity between two vectors
Range: [-1, 1]
In simple term, Cosine Similarity is the normalization (Euclidian Norm) of dot product

Cosine SImilarity

$Cosine Similarity = \frac{\sum_{i = 1}^{n} A_{i} B_{i}}{\sqrt{\sum_{i = 1}^{n} A_{i}^{2}} \sqrt{\sum_{i = 1}^{n} B_{i}^{2}}}$

-1 means opposite
1 means most similar
0 means no relation

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