Cost-Sensitive Machine Learning

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include:

• Cost of acquiring training data



• Cost of data annotation/labeling and cleaning



• Computational cost for model fitting, validation, and testing



• Cost of collecting features/attributes for test data



• Cost of user feedback collection



• Cost of incorrect prediction/classification

Cost-Sensitive Machine Learning is one of the first books to provide an overview of the cu...