A mobile phone carrier wants to predict whether a new customer is likely to switch to a competitor in the next three months. They plot customers based on usage patterns and support call frequency. The visualization below shows two groups: customers who stayed (green) and customers who left (orange). The black question mark represents a new customer whose status is unknown.

Which method should the team use if they want to classify the new customer based on the behavior of the closest existing customers in the plot?

Question

A mobile phone carrier wants to predict whether a new customer is likely to switch to a competitor in the next three months. They plot customers based on usage patterns and support call frequency. The visualization below shows two groups: customers who stayed (green) and customers who left (orange). The black question mark represents a new customer whose status is unknown.

Which method should the team use if they want to classify the new customer based on the behavior of the closest existing customers in the plot?

BlackTom AI · Accepted Answer

K-Nearest Neighbors (k‑NN) classifies a new observation by looking at the labeled examples closest to it in feature space and taking a majority vote of their labels.

1) Relevant knowledge points and concepts
- k‑NN is a lazy, instance‑based supervised classification algorithm: no global model is fitted; classification comes from labels of nearest neighbors.
- Key components: choice of k (number of neighbors), distance metric (e.g., Euclidean), and feature scaling (normalization).
- Alternatives: decision trees split on feature thresholds, logistic regression fits a global probability boundary, and k‑means is unsupervised clustering (no label voting).

2) Step‑by‑step reasoning
- Inspect the plot: labeled points are green = stayed, orange = left, and the question mark is the new customer.
- Measure distances from the question mark to nearby labeled points (conceptually or by Euclidean distance after scaling).
- Select k (commonly odd to avoid ties). Suppose k = 3 or 5.
- Count the labels among the k nearest neighbors. If the majority are green, predict “stayed”; if majority orange, predict “left”.

3) Example (intermediate steps)
- Imagine the three nearest points to ? are green, green, orange.
- Count: green = 2, orange = 1 → majority green → predict “stayed”.
- No algebraic calculation is needed beyond computing distances and counting labels.

4) Why the correct answer is correct
- The prompt explicitly asks for a method that classifies based on the behavior of the closest existing customers. That is the defining principle of k‑NN: classify by the nearest neighbors’ labels.

5) Why the other options are incorrect
- Decision Tree: builds hierarchical feature splits, not directly “nearest neighbor” voting.
- K‑Means Clustering: unsupervised; it groups points but does not use existing labels to vote for a specific class.
- Logistic Regression: fits a global linear (or transformed) decision boundary and does not classify purely by proximity to individual labeled points.

6) Conclusion
- Therefore the appropriate method is K‑Nearest Neighbors (k‑NN), which matches the provided correct answer.

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