Most of the time, relying on traditional metrics doesn’t capture the true financial impact of predictions like preventing a machine breakdown or optimizing maintenance schedules. What you need is custom metrics.
This blog dissects the core challenge of monitoring defect detection models: the censored confusion matrix. Additionally, I explore how business value metrics can help you articulate the financial impact of your ML models in front of non-data science experts.
Prevent costly machine breakdowns with NannyML’s workflow: Learn to tackle silent model failures, estimate performance with CBPE, and resolve issues promptly.
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