A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
The model is evaluated based on its ability to minimize the potential loss from customer churn. The cost of churn (losing a customer) is far greater than the cost of giving an incentive (to a customer who is predicted to churn but does not actually churn). Therefore, it is vital to reduce the number of false negatives, which represent customers who are incorrectly predicted not to churn but actually do churn. In this scenario, the confusion matrix shows 10 false positives (customers incorrectly predicted to churn) and 4 false negatives (customers incorrectly predicted not to churn). This results in higher false positives, which are less costly as per the given context. Hence, the correct justification is that the cost incurred by the company as a result of false positives is less than the false negatives.
Should it be C? Cost incurred by the company as a result of false positives (predicted churn, actual not churn) is less than the false negatives (predicted not churn, actual churn). Incentive cost < churn cost.
The question says "the cost of churn is far greater than the cost of the incentive", so we want to identify all the true churns, in order to do something about it. We don't want there to be any true churns we didn't see. This means we want false negatives as low as possible. So we want false negatives < false positives. So A.
Exactly, Count of False Positive should be greater than count of False Negative. In other words, cost / penalty for company is more when False Negative are predicted. So, Answer C - Cost incurred by the company as a result of False Positives is less than the False Negatives.
prefect explaination
No, the text clearly says the cost of churn is "far" greater - not equal to. One incident of churn could be higher than 10 incidents of incentive. Ans = A
According to your logic the answer is C. A false positive is no churn, a false negative is churn. So false negatives are the thing to avoid and are most expensive, hence, a false positive costs less than a false negative.
Fully Agree: FN = Predict Not churn, actual churn, high cost FP = Predict churn, actual not churn, pay incentive, low cost. so FP < FN, The answer is C.
A is right answer
FN < FP
Cost incurred by the company is directly proportional to cost of churn which is directly proportional to number of false negatives. False positives are more acceptable than false negatives in this case.
A is the correct answer
Cost FN > cost FP so want to minimize FN
The closest answer to this rationale is: A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives. Despite the answer options not matching the typical calculations of accuracy and precision, option A seems to be the most aligned with the company's goals if we consider the cost implications as more significant than the accuracy metrics alone. The company prefers a model has higher Recall score (10/14 this case 71.4%) than Precision score (10/20 this case 50%).
A) Because FN = 4 < FP = 10. FN are missed churns, and FP is misidentified churns.
Will go with C. My opinion is the same as brunokiyoshi
accuracy is 86% so A or C. The cost of losing a customer is very high. Thus we do not want False Negatives (we do not want to predict no churn when there is churn). Thus the cost of a false positive is less than a false negative. Answer C
The question says "the cost of churn is far greater than the cost of the incentive", so we want to identify all the true churns, in order to do something about it. We don't want there to be any true churns we didn't see. This means we want false negatives as low as possible. So we want false negatives < false positives and we get exactly that in the model. Now this fact coupled with the fact that incentives are welcome rather than churn, in other words, cost / penalty for company is more when False Negative are predicted. So, Answer C - Cost incurred by the company as a result of False Positives is less than the False Negatives.
cost of churn (churn cost) is greater than the cost of incentive (customers who do not churn)... the model predicts more false positives (customers who do not churn) than false negatives (customers who churn), Therefore, the costs of false negatives are greater than the costs of false positives, as churn is more expensive.
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Should be A. Since the cost of churn is much higher, the priority should be focused on minimizing FN and a viable model should be one with FN < FP, isn't it?
accuracy is 86% so A or C. The cost of losing a customer is very high. Thus we do not want False Negatives (we do not want to predict no churn when there is churn). Thus the cost of a false positive is less than a false negative. Answer C https://twtr.to/AWSCertifiedMachineLearningSpecialty
The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
A is Valid ( AAAAA )