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

TL;DR

This paper investigates how fluctuations in the parameter k of the K-Nearest Neighbors Regressor influence performance metrics like RMSE and R-squared, highlighting the sensitivity of distance-based regression models to this parameter.

Contribution

It provides an analysis of the effects of varying k on regression metrics, emphasizing the importance of parameter selection in KNN regression models.

Findings

Fluctuations in k significantly impact RMSE and R-squared values.

Visual representations show how metrics vary with different k values.

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.