In data mining workflows, which concept is used to designate the attribute that a predictive model should predict?

In data mining workflows, which concept is used to designate the attribute that a predictive model should predict?

• A. Feature
• B. Target
• C. Role
• D. Label

Answer: (C)

In data mining workflows, you designate how each attribute will be used in the modeling process by assigning a role. The role describes the function of a column, such as which one is the predictor (feature) and which one is the outcome to be predicted. The attribute that the model should predict is given the target role, signaling its purpose to the system. This makes the concept about assigning function to data fields central: it’s the role that tells the workflow which column is the outcome. While a feature is an input used for prediction and a target (or label) is the actual variable you aim to predict, the formal way to mark the column’s purpose across the workflow is its role. That’s why this option best captures the concept being tested.

In data mining workflows, you designate how each attribute will be used in the modeling process by assigning a role. The role describes the function of a column, such as which one is the predictor (feature) and which one is the outcome to be predicted. The attribute that the model should predict is given the target role, signaling its purpose to the system. This makes the concept about assigning function to data fields central: it’s the role that tells the workflow which column is the outcome.

While a feature is an input used for prediction and a target (or label) is the actual variable you aim to predict, the formal way to mark the column’s purpose across the workflow is its role. That’s why this option best captures the concept being tested.