Exercise

Relational Neighbor Classifier

In this exercise, you will apply a simple network based classifier called the relational neighbor classifier. It uses the class labels of neighboring nodes to compute a churn probability for each node in the network.
For example, in the network below where red nodes denote churners and white nodes denote non-churners, the churn probability of the blue node is 0.4.

Relational neighbor classifier

You are given two vectors: ChurnNeighbors and NonChurnNeighbors with each customer's number of neighbors that have churned and not churned, respectively.

Instructions

100 XP
  • Compute the churn probability of each customer, churnProb, using the relational neighbor classifier.
  • Use which() to find the customers with the highest probability of churning. Call this vector mostLikelyChurners.
  • Use mostLikelyChurners to find the IDs of the customers with the highest churn probability.