The correct answer is: C. at least one output attribute.
Supervised learning is a type of machine learning in which the model is trained on labeled data. This means that the model is given a set of data that includes both the input data and the desired output. The model then learns to map the input data to the output data.
Unsupervised learning is a type of machine learning in which the model is trained on unlabeled data. This means that the model is given a set of data that does not include the desired output. The model then learns to find patterns in the data without any guidance.
The main difference between supervised learning and unsupervised learning is that supervised learning requires a labeled dataset, while unsupervised learning does not.
Option A is incorrect because supervised learning does not require all input attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical input attributes.
Option B is incorrect because supervised learning does not require input attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical input attributes.
Option D is incorrect because supervised learning does not require output attributes to be categorical. In fact, many supervised learning algorithms can work with both categorical and numerical output attributes.