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 multi-objective optimization problem.
Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, making it a valuable tool in various applications.
It is especially useful in problems with a high imbalance in class distribution and a high imbalance in associated costs Cost-sensitive machine learning introduces a scalar cost function in order to find one (of multiple) Pareto optimal points in this multi-objective optimization problem.
Represented as a table, the matrix aligns true and predicted classes, assigning a cost value to each combination.
This approach allows practitioners to fine-tune models based on the specific consequences of misclassifications, adapting to scenarios where the impact of prediction errors varies across classes.
In the realm of data science, particularly in finance, cost-sensitive machine learning is applied to fraud detection.
In healthcare, cost-sensitive machine learning plays a role in medical diagnostics.
The approach allows for customization of models based on the potential harm associated with misdiagnoses, ensuring a more patient-centric application of machine learning algorithms.
A typical challenge in cost-sensitive machine learning is the reliable determination of the cost matrix which may evolve over time.