Jaccard Similarity
Jaccard Similarity is a measure of similarity between two sets of data. It is defined as the size of the intersection divided by the size of the union of the sets. In other words, it measures the proportion of shared data points between two sets. Jaccard Similarity is commonly used in data science and artificial intelligence for tasks such as clustering, classification, and recommendation systems. It is particularly useful when dealing with sparse data, where the majority of data points are missing. Jaccard Similarity can be used to compare text documents, images, and other types of data.
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