Which data mining model cannot predict a categorical target attribute?

Which data mining model cannot predict a categorical target attribute?

• A. Decision Tree
• B. Naive Bayes
• C. Logistic Regression
• D. k-Means Clustering

Answer: (D)

Predicting a categorical target relies on supervised learning, where the model learns from labeled examples to map features to class labels. K-means clustering is an unsupervised method that groups data by similarity without using any target labels, so it doesn’t produce a predicted category for new instances. In contrast, decision trees, Naive Bayes, and logistic regression are all supervised classification methods that learn from labeled data to assign new observations to discrete classes. Therefore, the model that cannot predict a categorical target attribute is k-means clustering.

Predicting a categorical target relies on supervised learning, where the model learns from labeled examples to map features to class labels. K-means clustering is an unsupervised method that groups data by similarity without using any target labels, so it doesn’t produce a predicted category for new instances. In contrast, decision trees, Naive Bayes, and logistic regression are all supervised classification methods that learn from labeled data to assign new observations to discrete classes. Therefore, the model that cannot predict a categorical target attribute is k-means clustering.