Author(s): Rohan Mistry

Originally published on Towards AI.

You Built a Model That Predicts Everything as “No.” It Has 95% Accuracy. You Just Shipped Garbage.

Your boss: “How’s the fraud detection model?”
You: “95% accuracy! Ready to deploy!”
Your boss: “Great! Ship it.”

The article discusses the common pitfalls of relying solely on accuracy as a metric for evaluating machine learning models, using relatable scenarios like fraud detection to illustrate how misleading a high accuracy may be when it does not correlate with actual useful outcomes. The author emphasizes that a model’s effectiveness should not just be measured by accuracy but by how well it achieves business goals and responds to imbalanced datasets, advocating for prioritizing metrics like recall and precision based on the context and implications of model predictions.

Read the full blog for free on Medium.

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Published via Towards AI