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Cost-sensitive machine learning
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<p>Cost-sensitive machine learning is an approach within <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> that considers varying costs associated with different <a href="/facts/Type_I_and_type_II_errors/aazbF3fq">types of errors</a>. This method diverges from traditional approaches by introducing a cost matrix, explicitly specifying the penalties or benefits for each type of prediction error. The inherent difficulty which cost-sensitive machine learning tackles is that minimizing different kinds of classification errors is a <a href="/facts/Multi-objective_optimization/1Hb4gjGJ">multi-objective optimization</a> problem. </p>