Distance Metrics Explained and Visualized in Python
Measuring the distance between two point is something almost trivial. However, there are many ways of doing that with each having it's strength!
Table of Content
Introduction: 0:00
Euclidean Distance Theory: 0:58
Euclidean Distance Code: 2:14
Euclidean Distance Visualization: 3:17
Manhattan Distance Theory: 5:02
Manhattan Distance Code: 7:08
Manhattan Distance Visualization: 7:41
Chebyshev Distance Theory: 8:52
Chebyshev Distance Code: 10:37
Chebyshev Distance Visualization: 11:03
Minkowski Distance Theory: 12:09
Minkowski Distance Code: 12:09
Minkowski Distance Visualization: 14:03
Mahalanobis Distance Theory: 14:39
Mahalanobis Distance Code: 17:22
Mahalanobis Distance Visualization: 18:02
Conclusion: 20:45
Github Link: https://github.com/yacineMahdid/artif...
There exists many kind of distance that can be used with continuous data. The most 5 most common are:
Euclidean Distance
Manhattan Distance
Chebyshev Distance
Minkowski Distance
Mahalanobis Distance
Each of them are related in some ways or another, with some being generalization. Using the right metric to describe the right distance in a problem set allows for having better results and/or better interpretation.
----
Join the Discord for general discussion: / discord
----
Follow Me Online Here:
Twitter: / codethiscodeth1
GitHub: https://github.com/yacineMahdid
LinkedIn: / yacine-mahdid-809425163
Instagram: / yacine_mahdid
___
Have a great week! 👋