<ul><li>Data scientists rely heavily on metrics like accuracy, precision, and recall, but sometimes these metrics can be misleading.</li><li>In a churn prediction project for a subscription app, achieving 89% accuracy initially seemed like a success, but the business was suffering from misfired retention campaigns and increased costs.</li><li>The team realized that accuracy alone was not a reliable metric and switched to AUC (Area Under the ROC Curve) which helped save the project.</li><li>This experience taught the team a $50,000 lesson and emphasized the importance of not trusting accuracy alone for model evaluation.</li></ul>

The One Metric That Changed Everything

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