Jaccard Index

Jaccard Index is a similarity measure that is used to compare the similarity and diversity of sample sets. It is defined as the size of the intersection of two sets divided by the size of the union of the sets. In other words, it measures the proportion of shared items between two sets. Jaccard Index is commonly used in data science and machine learning for tasks such as clustering, classification, and recommendation systems. It is particularly useful in text analysis, where it can be used to compare the similarity of two documents or sets of words. Jaccard Index is also used in social network analysis to measure the similarity of two users based on their shared connections.
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