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Industrial Failure Classifier

Business-cost-tuned failure prediction · AI4I 2020 · XGBoost + SHAP

⏳ Checking API…

Sensor Readings

Enter current sensor values. The model predicts failure probability using a business-cost-optimal threshold (FN=$50K, FP=$2K).

How Threshold Tuning Works

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Asymmetric Costs

Missing a real failure costs $50,000 in unplanned downtime. A false alarm costs $2,000 in unnecessary maintenance. These are very different errors.

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Default 0.5 is Wrong

The default threshold maximizes accuracy — which is misleading when failures are only 3.4% of records. 97% accuracy by always predicting "no failure" is useless.

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Cost-Optimal Threshold

The optimal threshold minimizes: FN × $50K + FP × $2K. In the current model, that moves the operating threshold to 0.775, reducing false alarms while keeping recall high.