论文标题
通过非参数回归模型估算医疗保险报销之间的延时
Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models
论文作者
论文摘要
非参数监督学习算法代表了一类简洁的监督学习算法，其中学习参数高度灵活，其价值直接取决于培训数据的大小。在本文中，我们相对研究了四种非参数算法，K-Nearest邻居（KNN），支持向量机（SVM），决策树和随机森林的特性。监督的学习任务是对医疗保险报销时间延时的回归估计。我们的研究完全关注每个非参数回归模型的适合训练数据。我们使用R平方度量来量化拟合的优点。结果介绍了训练数据大小，特征空间维度和超参数优化的效果。
Non-parametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time-lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization.
