Cost-Sensitive Learning is a machine learning approach that takes into account the different costs associated with misclassifying different classes. In many real-world applications, the consequences of errors can vary significantly; for example, failing to identify a disease in a patient may have a higher cost than incorrectly diagnosing a healthy individual. By incorporating these costs into the learning process, models can be trained to minimize the overall cost rather than just maximizing accuracy. This technique is particularly useful in fields like finance, healthcare, and fraud detection, where the implications of misclassification can lead to substantial losses or risks. Cost-sensitive algorithms adjust their decision-making criteria based on the assigned costs, allowing for more informed and effective predictions tailored to specific scenarios.