Paper ID: 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

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.

Submitted: Aug 24, 2022