ABSTRACT
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
TABLE OF CONTENTS
part I|2 pages
I Theoretical Underpinnings of Cost-Sensitive Machine Learning
part II|2 pages
II Cost-Sensitive Machine Learning Applications
