Evaluation · Flagship experience

Predictive Uncertainty

When should a model say “I am not sure”?

Start here

When should a model say “I am not sure”?

Point predictions hide uncertainty from noise, limited data and model uncertainty. Intervals or predictive distributions make that uncertainty visible.

Building interactive view…

Understand

Build the mental model

Point predictions hide uncertainty from noise, limited data and model uncertainty. Intervals or predictive distributions make that uncertainty visible. Evaluate interval coverage and width, not only point error. Escalate or abstain when uncertainty is operationally important.

What happens if…?

Break the assumption deliberately

Move a test point far outside the training region and compare point prediction confidence with uncertainty.

Experiment intensity45%

Move the control and explain what you expect before reading the visual.

Technical lens

Formalise what the visual is doing

Aleatoric uncertainty comes from irreducible outcome variation; epistemic uncertainty reflects limited knowledge. Conformal methods can provide coverage guarantees under assumptions.

Technical questionUse a tiny case to make the mechanism observable. Aleatoric uncertainty comes from irreducible outcome variation; epistemic uncertainty reflects limited knowledge. Conformal methods can provide coverage guarantees under assumptions. Verify one intermediate quantity, state change or mapping independently; then predict the consequence of this change: Move a test point far outside the training region and compare point prediction confidence with uncertainty.

Practitioner lens

Use it responsibly

Evaluate interval coverage and width, not only point error. Escalate or abstain when uncertainty is operationally important.

Transfer testMonitoring only server uptime instead of model/data behaviour.

Worked exploration

Use the visual as an experiment, not decoration

Technical lens

Aleatoric uncertainty comes from irreducible outcome variation; epistemic uncertainty reflects limited knowledge. Conformal methods can provide coverage guarantees under assumptions.

Practitioner check

Evaluate interval coverage and width, not only point error. Escalate or abstain when uncertainty is operationally important.

Prediction before interaction

Move a test point far outside the training region and compare point prediction confidence with uncertainty.

Exploration walkthrough

Turn the interaction into an evidence trail

Give a point prediction of 100 and an interval of 80–125. Create an in-distribution and an unfamiliar case; a useful uncertainty mechanism should often widen or flag uncertainty for the unfamiliar case. Before moving the control, state your prediction. After the visual changes, name the specific state, statistic, boundary or mapping that changed and explain why that change is consistent—or inconsistent—with your prediction.

Record one observable quantity before the interaction and the same quantity afterwards.
Change one factor at a time so the causal effect of the control is inspectable.
Use an edge or failure case to discover where the concept stops behaving as the simple story suggests.

Reference depth

Open the complete material

The flagship experience is the map. These pages contain the roads.

Deep Learning Hub lessons

Aleatoric uncertaintyPredictive Uncertainty Epistemic uncertaintyPredictive Uncertainty Predictive intervalsPredictive Uncertainty Conformal predictionPredictive Uncertainty Coverage and interval widthUncertainty & Intervals

Related models & simulations

Gaussian Process Regressor (GPR)Interactive model page Gaussian Process Classifier (GPC)Interactive model page

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These destinations are explicitly mapped to Predictive Uncertainty; they are not generic landing-page fallbacks.