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 nonparametric 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 nonparametric 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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Gupta, A., Joshi, R., Kanvinde, N., Gerela, P., Laban, R.M. (2023). Metric Effects Based on Fluctuations in Values of k in Nearest Neighbor Regressor. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_12
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