Abstract
AbstractIn Machine Learning, prediction quality is usually measured using different techniques and evaluation methods. In the regression models, the goal is to minimize the distance between the actual and predicted value. This error evaluation technique lacks a detailed evaluation of the type of errors that occur on specific data. This paper will introduce a simple regularization term to manage the number of over-predicted/under-predicted instances in a regression model.
Publisher
Springer Science and Business Media LLC
Subject
Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems
Reference30 articles.
1. Ito S, Fujimaki R. Optimization beyond prediction: Prescriptive price optimization. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1833–1841.
2. Morris S, Kumari T. Overestimation in the growth rates of national income in recent years?–an analyses based on extending gdp04–05 through other indicators of output. Indian Institute of Management Ahmedabad. 2019;380:15.
3. Xie Z-R, Chen J, Wu Y. Predicting protein–protein association rates using coarse-grained simulation and machine learning. Sci Rep. 2017;7(1):1–17.
4. Sheiner E, Sheiner EK, Hershkovitz R, Mazor M, Katz M, ShohamVardi I. Overestimation and underestimation of labor pain. Eur J Obstetr Gynecol Reprod Biol. 2000;91(1):37–40.
5. Armstrong TB, Koles´ar M, Kwon S. Bias-aware inference in regularized regression models. arXiv preprint arXiv:2012.14823. 2020.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献