9 Distance Measures in Data Science

By Medium - 2021-02-01

Description

Exploring the advantages and pitfalls of 9 common distance measures used in Machine Learning applications.

Summary

Daily Read 9 Distance Measures in Data Science The advantages and pitfalls of common distance measures Many algorithms, whether supervised or unsupervised, make use of distance measures.
Moreover, you can also use Hamming distance to measure the distance between categorical variables.
In practice, it is the total number of similar entities between sets divided by the total number of entities.
Intuition is important in distance measures as it allows for better usage of the metric without v The Jaccard index (or Intersection over Union) is a metric used to calculate the similarity and diversity of sample sets.

Topics

Backend (0.25)
NLP (0.17)
Database (0.14)

Similar Articles

Calculating Document Similarities using BERT, word2vec, and other models

By Medium - 2020-12-03

Document similarities is one of the most crucial problems of NLP. Finding similarity across documents is used in several domains such as recommending similar books and articles, identifying…

DIY Stride Analysis driven by Machine Learning to Compare the Next Generation of Running Shoes

By Medium - 2020-12-24

I wanted to try using machine learning to do a backyard experiment around analyzing running stride. I put together a setup with only my phone, a tripod, treadmill, my laptop, and a fast runner. My…

Data normalization in machine learning

By Medium - 2020-12-14

What is it, how does it help, tools used and an experiment

Data Transformation: Standardization vs Normalization

By KDnuggets - 2021-03-14

Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and ...