H13-311_V3.5 · Question #261
With a lot of sales data but no labels, companies want to identify VIP Customer, the following model Suitable?
The correct answer is C. K-Means D. Hierarchical clustering. Identifying VIP customers from unlabeled data is an unsupervised learning problem - you're discovering natural groupings without predefined categories. K-Means (C) and Hierarchical Clustering (D) are both unsupervised clustering algorithms that can segment customers into groups…
Question
With a lot of sales data but no labels, companies want to identify VIP Customer, the following model Suitable?
Options
- ALogistic regression
- BSVM
- CK-Means
- DHierarchical clustering
How the community answered
(37 responses)- A19% (7)
- B8% (3)
- C73% (27)
Explanation
Identifying VIP customers from unlabeled data is an unsupervised learning problem - you're discovering natural groupings without predefined categories. K-Means (C) and Hierarchical Clustering (D) are both unsupervised clustering algorithms that can segment customers into groups (e.g., high-spenders, frequent buyers) without needing labeled examples, making them ideal here.
Logistic Regression (A) and SVM (B) are both supervised classification models - they require labeled training data (e.g., "this customer is VIP / not VIP") to learn a decision boundary, which directly contradicts the "no labels" constraint in the question.
Memory tip: If the question mentions "no labels," immediately eliminate any supervised model (regression, SVM, neural nets trained with labels). Only unsupervised methods - clustering (K-Means, hierarchical, DBSCAN) or dimensionality reduction - can work blind.
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