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arXiv · 2208.11540

Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor

Abhishek Gupta·Raunak Joshi·Nandan Kanvinde·Pinky Gerela·Ronald Melwin Laban
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Abstract

Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression model. The distance based model used is K-Nearest Neighbors Regressor which is a supervised non-parametric method. The point that we want to prove is the effect of k parameter of the model and its fluctuations affecting the metrics. The metrics that we use are Root Mean Squared Error and R-Squared Goodness of Fit with their visual representation of values with respect to k values.

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Abhishek Gupta, Raunak Joshi, Nandan Kanvinde, Pinky Gerela, Ronald Melwin Laban. 2022-08-24. Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor. https://doi.org/10.1007/978-981-19-6004-8_12

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