# What is the Jaccard Similarity?

The Jaccard similarity coefficient, or Jaccard Index, is a measure of similarity between two sets of data. It is calculated by taking the size of the intersection of the two sets and dividing it by the size of the union of the two sets. This gives us a value between 0 and 1, where 0 indicates no similarity and 1 indicates perfect similarity.

The Jaccard similarity coefficient can be used in machine learning and data science in a variety of ways. For example, it can be used to measure the similarity between two documents, or to measure the similarity between two sets of data points. It can also be used to measure the similarity between two clusters of data points, or to measure the similarity between two sets of features. In addition, it can be used to measure the similarity between two sets of words or phrases.

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What is the Tanimoto Similarity?