The Jaccard index (or Intersection over Union) is a metric used to calculate the similarity and diversity of sample sets. It is the size of the intersection divided by the size of the union of the sample sets.
In practice, it is the total number of similar entities between sets divided by the total number of entities.
To calculate the Jaccard distance we simply subtract the Jaccard index from 1
highly influenced by the size of the dat
Large datasets can have a big impact on the index as it could significantly increase the union whilst keeping the intersection similar
The Jaccard index is often used in applications where binary or binarized data are used
deep learning model predicting segments of an image
text similarity analysis to measure how much word choice overlap there is between documents