Distance Metrics for Machine Learning and it's Relation with Other Distances
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Page Numbers:
15-23
Digital Object Identifier:
10.58578/mjms.v1i1.1990
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Authors:
Dipendra Prasad Yadav1, Nand Kishor Kumar2, Suresh Kumar Sahani3* 
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Abstract
In machine learning, distance metrics play a crucial role in measuring the degree of dissimilarity among data points. When creating and optimizing machine learning models, data scientists and machine learning practitioners can make more informed choices by understanding the features of popular distance metrics and their relationships. The effectiveness and interpretability of the model's output can be greatly influenced by selecting the appropriate distance metric. We explain distance metrics and their relevance in machine learning with various examples of metrics, including Minkowski distance, Manhattan distance, Max Metric for R^n, Taxicab distance, Relative distance, and Hamming distance.
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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Kreyszing, E. (1973). Introductory Functional Analysis with Applications. New York: John Wiley & Sons, 1-7.
Croom, F. H. (1989). Principles of Topology. USA: Rinehart and Winston, Ins. 4,55-60.
Sharma, N. (2019). Importance of Distance Metrics in Machine Learning Modeling. Towards Data Science.
Cosine Similarity- Sklearn TDS article Wikipedia Example.
Distance Metrics- Math.net, Wiki
Minkowski Distance Metric- Wiki, Blog, Famous Metrics.
Lavrentiev,M. (1929). Sur la correspondence entre les frontiers dans la representation conform. Rec.Math,36,112-115.
Haming, R.W. (1950). Error detecting and error correcting codes. The Bell System Technical Journal,29(2),147-160.