Description: A Dummy classifier is a machine learning model that makes predictions using simple and basic rules, without considering the complexity of the data. Its main function is to serve as a reference point or baseline for evaluating the performance of other, more sophisticated models. This type of classifier can use strategies such as predicting the most frequent class in a dataset or randomly assigning classes. Although its simplicity may seem like a disadvantage, its utility lies in its ability to establish a minimum performance standard. Dummy classifiers are especially valuable in situations where data is imbalanced, as they allow researchers and developers to understand whether a more complex model is truly adding value compared to a trivial strategy. In summary, the Dummy classifier is a fundamental tool in machine learning that helps contextualize and evaluate the effectiveness of more advanced models.
