Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor
This is an incremental study for researchers using distance-based regression models, focusing on parameter tuning effects.
The paper investigates how fluctuations in the k parameter affect the performance metrics of the K-Nearest Neighbors Regressor, specifically analyzing Root Mean Squared Error and R-Squared Goodness of Fit with visual representations.
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.