Mini toolsDefinition
A learning approach that takes into account different misclassification costs for different types of errors.
Detailed Explanation
Cost-sensitive learning incorporates varying penalties for different types of prediction errors into the learning process. This approach is particularly useful when certain types of mistakes are more expensive or dangerous than others. It can be implemented through modified loss functions sample weighting or specialized algorithms.
Use Cases
Medical diagnosis financial risk assessment industrial quality control
