  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.
