[TBD]Selecting right distance metric in machine learning

Introduction

Laymen explanation

Distance measures play an important role in machine learning.

They provide the foundation for many popular and effective machine learning algorithms like k-nearest neighbors for supervised learning and k-means clustering for unsupervised learning.

Technical explanation

Distance measures play an important role in machine learning. A distance measure is an objective score that summarizes the relative difference between two objects in a problem domain.

Both supervised and unsupervised algorithms in machine learning use Distance Metrics to understand patterns in the input data. This metric is also used for the identification of similarities between results. A strong distance metric helps to dramatically improve the efficiency of Clustering or Classification algorithm’s performance.

Types

• • Mahalanobis distance

• Hamming Distance

• Euclidean Distance

• Manhattan Distance

• Minkowski Distance

Importance of selecting right distance metric

If you select right distance metric for K-NN classifier, then scaling is not needed. However, scaling is needed if Euclidean distance metric is used.

Selection criteria for distance metric

• • Algorithm selected

  • probability distribution model of dataset(verify)

  • Whether output cluster is convex or not(verify)

Reference

https://machinelearningmastery.com/distance-measures-for-machine-learning/

https://sites.google.com/site/jbsakabffoi12449ujkn/home/machine-intelligence/role-of-normalisation-in-machine-learning

https://www.researchgate.net/post/How-to-choose-the-right-distance-method

https://stats.stackexchange.com/questions/109597/how-to-choose-the-right-distance-matrix-for-clustering