True or False: Data mining modeling techniques can classify, predict, or both.

True or False: Data mining modeling techniques can classify, predict, or both.

• A. Sometimes
• B. False
• C. True
• D. Depends on data

Answer: (C)

In data mining, modeling techniques are used to produce outcomes that can be either class labels or numeric predictions. Classification assigns each instance to a discrete category, while prediction (often called regression) estimates a continuous value. Many techniques can do either task, depending on how you frame the problem and what you set as the target variable. For example, a decision tree or a neural network can be used to classify emails as spam or not, and the same approach can be adapted to predict house prices if you target a numeric value. Algorithms like random forests or gradient boosting can be configured for both classification and regression tasks. Because these modeling methods are capable of delivering either type of output, they can classify, predict, or both.

In data mining, modeling techniques are used to produce outcomes that can be either class labels or numeric predictions. Classification assigns each instance to a discrete category, while prediction (often called regression) estimates a continuous value. Many techniques can do either task, depending on how you frame the problem and what you set as the target variable. For example, a decision tree or a neural network can be used to classify emails as spam or not, and the same approach can be adapted to predict house prices if you target a numeric value. Algorithms like random forests or gradient boosting can be configured for both classification and regression tasks. Because these modeling methods are capable of delivering either type of output, they can classify, predict, or both.