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

The question asks which method should be used to classify a new customer based on the behavior of the closest existing customers in the plot.
Option 1: Decision Tree. This method builds a tree to split data based on feature thresholds and is more suited for structured, rule-based classification. It does not inherently base the class of a new point on the nearest existing points in the feature space, so it’s not the best fit for this proximity-based task.
Option 2: K-Means Clustering. This is an unsupervised learning technique used to partition data into k clusters based on similarity. It assigns points to clusters without using labeled outcomes (stay vs. left). Since the goal is to predict a label for the new customer, and we have known labels for existing customers, k-means is not appropriate for supervised classification here.
Option 3: K-Nearest Neighbors (k-NN). This method classifies a new observation by looking at the k closest labeled examples in the feature space and taking a majority vote (or weighted vote). Given the visualization showing green (stayed) and orange (left) groups, using the nearest neighbors to decide the new customer’s status aligns exactly with proximity-based classification.
Option 4: Logistic Regression. This supervised model estimates the probability of a class based on a linear combination of features. While it can be used for classification, it does not inherently rely on the closest neighbors; instead, it fits a global decision boundary. In this scenario, the direct nearest-neighbor intuition is more appropriate than a global linear boundary.
In summary, option 3 (K-Nearest Neighbors) best matches the described approach of classifying a new point by the behavior of its closest existing customers, while the other options do not align with the proximity-based, neighbor-driven classification shown in the plot.

IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

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