The correct answer is: C. input attribute.
Supervised learning and unsupervised clustering both require at least one input attribute. An input attribute is a feature that is used to train a model. In supervised learning, the input attributes are used to predict an output attribute. In unsupervised clustering, the input attributes are used to group data points together.
A hidden attribute is an attribute that is not directly observed, but can be inferred from the observed attributes. A categorical attribute is an attribute that can take on a finite number of values.
Here is a more detailed explanation of each option:
- A. hidden attribute
A hidden attribute is an attribute that is not directly observed, but can be inferred from the observed attributes. For example, in the problem of predicting whether a customer will churn, the hidden attribute might be the customer’s satisfaction with the product.
- B. output attribute
An output attribute is an attribute that is predicted by a model. In supervised learning, the output attribute is the target variable that the model is trying to predict. In unsupervised clustering, the output attribute is the cluster label that is assigned to each data point.
- C. input attribute
An input attribute is a feature that is used to train a model. In supervised learning, the input attributes are used to predict the output attribute. In unsupervised clustering, the input attributes are used to group data points together.
- D. categorical attribute
A categorical attribute is an attribute that can take on a finite number of values. For example, the attribute “gender” can take on the values “male” and “female”.
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