What is Prediction Drift Monitoring?

Definition
Prediction drift monitoring tracks changes in the distribution of model outputs over time, detecting when predictions shift even if the model itself has not changed.
WHY MONITOR PREDICTIONS
Prediction drift provides a signal without waiting for labels. If the distribution of model outputs changes significantly—more high-confidence predictions, shift toward certain classes, different score ranges—something has changed. This might indicate data drift, concept drift, or upstream issues.
Prediction drift is observable immediately. Unlike performance metrics that require ground truth, you can compute prediction statistics in real-time. This makes prediction drift a valuable early warning signal.
WHAT TO TRACK
Score distribution: For classifiers, track predicted probability distribution. Mean, std, percentiles (p10, p50, p90). A shift in mean score or narrowing of distribution indicates change.
Class distribution: Track fraction predicted to each class. If a binary classifier suddenly predicts 80% positive when it was 50%, something changed.
Confidence distribution: Track how confident the model is. A drop in confidence (more predictions near 0.5) suggests the model is encountering unfamiliar data.
RELATIONSHIP TO OTHER DRIFT TYPES
Prediction drift is downstream of data drift. Data drift (inputs change) typically causes prediction drift (outputs change). But prediction drift can occur without measurable data drift if feature interactions change in ways not captured by univariate monitoring.
Prediction drift is not the same as performance drift. Predictions can shift while performance remains stable (if the ground truth also shifted proportionally). Performance drift requires labels to measure.
💡 Key Insight: Prediction drift is your fastest feedback signal. It does not require labels and reflects how the model responds to changing inputs.

💡 Key Takeaways

✓Prediction drift: changes in model output distribution; observable immediately without labels

✓Track: score distribution (mean, std, percentiles), class distribution (fraction per class), confidence distribution

✓Prediction drift is downstream of data drift; can occur without measurable univariate data drift due to interactions

📌 Interview Tips

1Interview Tip: Explain why prediction drift is faster signal than performance drift—no labels needed.

2Interview Tip: Give example: binary classifier suddenly predicting 80% positive when baseline was 50%.

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