All of the following are responsibilities of derivative classifiers except

Derivative classifiers are responsible for organizing and categorizing data. They are not responsible for making decisions based on the data.

Explanation

Derivative classifiers are important tools for analyzing and understanding data. By organizing and categorizing data, derivative classifiers enable us to make better decisions about how to use the data. For example, a derivative classifier might be used to classify customer data so that appropriate marketing strategies can be implemented.

The other types of classifiers mentioned – decision trees, support vector machines, and logistic regression – are used to make decisions based on the data. These classifiers are often used to predict outcomes, such as whether a customer will make a purchase, or whether a patient is likely to have a certain illness.

Ultimately, derivative classifiers are used to organize and categorize data so that we can make better decisions about how to use it. By understanding how derivative classifiers work, we can better understand how to make use of data and make better decisions.