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ID vs OOD Score Distributions

Two overlapping kernel density curves showing ID/OOD separability with mean markers and threshold line.

When to use this prompt

For OOD detection / anomaly detection / safety-evaluation papers.

The prompt

A kernel density plot showing two overlapping distributions of an anomaly score R.

X-axis: Score R, range 0 to 5, gridlines every 1.
Y-axis: Density, range 0 to 1.5.

Two distributions:
- ID (in-distribution): blue curve, mean 0.7, std 0.4. Filled at 25% opacity.
- OOD (out-of-distribution / hazard): coral curve, mean 2.5, std 0.8. Filled at 25% opacity.

Annotations:
- Vertical dashed line at the chosen threshold R = 1.5 with label "Threshold (95th percentile of ID)".
- Vertical solid lines at each distribution's mean, labeled "ID mean" and "OOD mean".
- Top-right legend with the two distributions and an additional line showing the AUROC value (e.g., AUROC = 0.96).

A small inset on the bottom-right shows ROC for the same scores.

Style: clean academic chart, white background, sans-serif labels, restrained palette. Suitable for anomaly detection figures and safety evaluation reports.

Variations

Per-hazard-type breakdown

Replace the single OOD curve with three OOD curves (one per hazard type) and re-color them. Compute AUROC per hazard type and list each in the legend.

Tips

  • Use translucent fills with thicker borders. Solid fills hide the overlap region.
  • Annotate threshold and mean lines. Without them the plot is just two blobs.
  • Show AUROC explicitly somewhere on the plot — readers expect it for binary separability.

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