Operational Controls Live Model Pipeline
Trained logistic boundary optimizing asymmetric cost weights across both dimensions.
Decision Threshold (τ)
0.50
Sensor Noise / Entropy (σ)
0.25
Sample Size (n)
400
Positive Rate / Imbalance
15%
Why data science? Human intuition routinely relies on one-dimensional thresholds (e.g. "Flag when Temperature > 80"), ignoring cross-feature covariance and catastrophic false negative penalties. Data modeling learns the exact calibrated decision boundary.
Feature Space & Frontier
Normal
Failure Event
Boundary
Sensor Feature X (Vibration Spectrum)
Drag slider or click boundary to re-weight penalties
Sensor Feature Y (Thermal Rise)
Business Impact & ROI Cost Model
Data Science Value Dividend
+$14,250 net saved
Data science modeling eliminates 78% of costly False Negatives compared to intuition heuristics.
Confusion Matrix
Total Cost: $6,300
True Positive (TP)
54
Gain: +$100 ea
False Positive (FP)
18
Loss: -$50 ea
False Negative (FN)
6
Critical: -$500 ea
True Negative (TN)
322
Cost: $0
Precision
75.0%
Recall (Sensitivity)
90.0%
Specificity
94.7%
F1 Score
0.818
ROC Space Operating Point
AUC: 0.94