Similarity Metrics

What are Similarity Metrics?

Similarity Metrics are mathematical measures used to quantify the similarity or dissimilarity between objects, such as vectors, strings, or sets. In machine learning and data analysis, similarity metrics are often used to compare data points, cluster similar items, or retrieve similar items from a database.

Common similarity metrics include:

  1. Euclidean Distance: Measures the straight-line distance between two points in Euclidean space.
  2. Cosine Similarity: Measures the cosine of the angle between two vectors, which is a measure of their orientation similarity.
  3. Jaccard Similarity: Measures the similarity between sets by comparing the size of their intersection to the size of their union.
  4. Hamming Distance: Measures the number of differing elements between two strings of equal length.
  5. Levenshtein Distance (Edit Distance): Measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another.

Additional resources on Similarity Metrics: