论文标题
基于k值的波动基于最近邻域回归剂的波动效应
Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor
论文作者
论文摘要
机器学习的回归分支纯粹集中在连续值的预测上。监督学习分支具有许多基于参数和非参数学习模型的基于回归的方法。在本文中，我们旨在针对与基于距离的回归模型相关的非常微妙的点。所使用的基于距离的模型是K-Nearest邻居回归器，它是一种监督的非参数方法。我们要证明的观点是模型的k参数的效果及其影响指标的波动。我们使用的指标是根平方误差和R平方的拟合优度与它们相对于k值的值表示。
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.
