Error and optimism bias regularization

Abstract

Abstract In 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.

Keywords

• Regression
• Regularization
• Cost function
• Optimism bias
• Over-estimation
• Under-estimation