Probabilistic Relational Neighbor Classifier

In this exercise, you will apply the probabilistic relational neighbor classifier to infer churn probabilities based on the prior churn probability of the other nodes.

Instead of knowing the nodes' labels, suppose you know each node's probability of churn, as in the image below. In the image, C stands for churn and NC for non-churn. Then, as before, you can update the churn probability of the nodes by finding the average of the neighboring nodes' churn probabilities.
Probabilistic relational neighbor classifier

Find the churn probability of the 44th customer in the vector churnProb.
Update the churn probability by multiplying AdjacencyMatrix by churnProb and dividing with the vector neighbors which contains the neighborhoods' sizes. We have added as.vector() around the matrix operations. Assign the result to churnProb_updated.
Find the updated churn probability of the 44th customer in the vector churnProb_updated.
What happened to the churn probability of the 44th customer?.

Exercise

Probabilistic Relational Neighbor Classifier

Instructions