The correct answer is: B. deductive
The deductive view of classification is a way of thinking about categories that allows us to associate a probability of membership with each classification. This is in contrast to the classical view of classification, which does not allow for probabilities.
The deductive view of classification is based on the idea that categories are defined by a set of features. For example, the category “dog” might be defined by the features “has four legs,” “has fur,” and “barks.” If an object has all of these features, then it is a member of the category “dog.” However, if an object only has some of these features, then it may or may not be a member of the category “dog.”
The deductive view of classification is useful for many tasks, such as natural language processing and machine learning. For example, if we are trying to build a system that can classify images of dogs, we can use the deductive view of classification to define the features that we want the system to look for. Then, we can train the system on a set of images of dogs and non-dogs. The system will learn to associate each feature with a probability of membership in the category “dog.”
The deductive view of classification is not without its limitations. One limitation is that it can be difficult to define the features that define a category. For example, it is not always clear what features should be used to define the category “dog.” Another limitation is that the deductive view of classification does not allow for exceptions. For example, if we define the category “dog” as “has four legs,” “has fur,” and “barks,” then a dog that has three legs would not be a member of the category “dog.”
Despite its limitations, the deductive view of classification is a powerful tool that can be used to solve many problems.