Why Accuracy Can Be Deceptive: A Product Manager's Guide to Precision and Recall

As product managers, we rely on data to make informed decisions. But when it comes to classification models, a common metric like accuracy can sometimes be misleading.Let me share an insightful experience.

The Case of the Deceptive Dummy Model

I was working on a credit risk assessment problem, where the objective was to create a system that could predict loan applications likely to default. Initially, I built a naive model that simply predicted no one would default, resulting in a seemingly impressive 91% accuracy. However, this was misleading because approximately 91% of applicants don't default statistically.But this doesn't tell the whole story.

This raised concerns about the reliability and trustworthiness of accuracy as a metric in such scenarios.

Introducing Precision and Recall

The metrics that product managers should understand are precision and recall.

To illustrate, let's consider a simple example:

Set = 1, 2, 3, 4, 5, 6, 7, 8, 9,10

Model= Y, N, N, N, Y, N, N,Y,Y,N

Reality= Y,N,N,N, N, Y, N,N, N, Y

In this scenario, the recall (the proportion of actual positives correctly identified) is 1/3 (33%), and the precision (the proportion of positive predictions that are correct) is 1/4 (25%).

Now, let's explore different scenarios:

• If the model predicts 'Y' for all instances, the recall becomes 100%, but the precision drops to 3/10.
• If the model predicts 'N' for all instances, the recall becomes 0%, and precision is undefined.

The Precision-Recall Trade-off

Suppose we have two models:

Model A with 95% recall and 80% precision, and

Model B with 87% recall and 85% precision. Which one should you choose?

As a product manager, you need to understand the business requirements and decide accordingly. For example, to asses the loan defaulter case; if your organization uses the model to flag applications for manual verification and has sufficient staff to handle false positives, Model A might be preferred due to its high recall, ensuring most potential defaulters are flagged.

Real-World Examples:

• Fraud Detection: A high precision model flags only suspicious transactions, minimizing customer inconvenience. However, a high recall model ensures catching most fraudulent transactions, protecting your business.
• Medical Diagnosis: A high recall test for a critical illness identifies most positive cases, even if it leads to some unnecessary follow-up tests. High precision ensures a positive result truly indicates the illness.

The Takeaway

Understanding precision and recall empowers you to choose the model that best aligns with your product's goals. Consider the cost of errors: is a missed defaulter more damaging than a wrongly flagged application? By weighing these factors, you can make data-driven decisions that optimize your product's performance.

Let's discuss! What are some other examples where precision and recall are crucial for product managers? Share your thoughts in the comments!