Supervised learning differs from unsupervised clustering in that supervised learning requires
A) at least one input attribute.
B) input attriutes to be categorical.
C) at least one output attribute.
D) ouput attriubutes to be categorical.

Question

Quizplus · Accepted Answer

Supervised learning requires at least one output attribute, which is used as the target variable for the learning algorithm. Unsupervised clustering, on the other hand, does not require any output attributes and instead attempts to group data based on similarities and differences in the input attributes. Choices A and B are incorrect because input attributes can be either numerical or categorical. Choice D is incorrect because output attributes can also be numerical.

Supervised Learning Differs from Unsupervised Clustering in That Supervised Learning