![]() For example, Euclidean distance, Manhattan distance, etc. There are many other measures of distances between two lists of values. Let’s use the above function we created to calculate the Jaccard Distance between two lists. It is defined as one minus the Jaccard Similarity. It is used as a measure of how dissimilar two sets of values are. Let’s now pass two lists of integers to the above function. Jaccard Similarity between two lists of integers We get ~0.14 as the output, which is the same result we got from manual calculation above. Let’s pass two lists of strings to the above function to get the Jaccard Similarity between them. Jaccard Similarity between two lists of strings Let’s now see the above code in action with the help of some examples. J = float(len(a.intersection(b))) / len(a.union(b)) Now that we know how Jaccard Similarity is calculated, we can write a custom function to Python to compute the Jaccard Similarity between two lists. ![]() You can see that we get the Jaccard Similarity between the two tweets as 0.14. Now, let’s say we apply some preprocessing to the above sentences – all lowercase, remove punctuations and tokenize the sentence into set of words, we get the following two sets.Īccording to the formula, we need to determine the number of items in the intersection and the union of the two sets and divide the two to get the Jaccard Similarity. One word: Doge- Elon Musk December 20, 2020 No highs, no lows, only Doge- Elon Musk February 4, 2021 Let’s calculate the Jaccard Similarity between these tweets. The higher the similarity, the more similar the two sets are. It is defined as the fraction of number of common elements in two sets to the total number of elements in the union of the two sets. Jaccard Similarity is a measure of how similar two sets are based on the items present in both the sets. We will also look at Jaccard Distance, another metric that is commonly used with the help of some examples. In this tutorial, we will look at what is Jaccard Similarity and how to calculate it in Python. For example, how similar two tweets are based on the contents of the tweets. Jaccard Similarity is commonly used to evaluate how similar two pieces of texts are.
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