Conformal Prediction
A framework-agnostic method that provides predictions with guaranteed confidence intervals without assumptions about model distribution.
Conformal prediction provides guaranteed confidence intervals for any ML model – without distributional assumptions, only from calibration data.
Explanation
Conformal prediction produces prediction sets instead of point predictions. At chosen confidence level α, the set contains the true value with probability 1-α – distribution-free and model-agnostic.
Marketing Relevance
For risk-sensitive marketing decisions (budget forecasts, conversion predictions), conformal prediction provides reliable uncertainty ranges.
Example
A conversion model doesn't predict "42% probability" but "with 90% certainty between 35% and 49%".
Common Pitfalls
Large prediction sets at high uncertainty are hard to interpret. Exchangeability assumption can be violated for time series.
Origin & History
Vladimir Vovk developed conformal prediction in the 2000s. From 2020, it gained massive popularity through work by Angelopoulos & Bates. MAPIE (2022) made it accessible for Python users.
Comparisons & Differences
Conformal Prediction vs. Bayesian Inference
Bayesian inference requires prior assumptions and distribution models; conformal prediction is distribution-free with frequentist guarantees.
Conformal Prediction vs. Calibration
Calibration adjusts probabilities post-hoc; conformal prediction produces sets with formal coverage guarantees.