Conformal Prediction

Conformal prediction is a distribution-free framework that wraps any predictive model to produce prediction intervals or sets with guaranteed finite-sample coverage under exchangeability of the data.

컨포멀 예측은 어떤 예측모형에도 적용하여 데이터의 교환가능성 가정 아래 유한표본에서 보장된 포함률을 갖는 예측구간이나 예측집합을 만드는 분포무관 방법론이다.

How do I get prediction intervals with guaranteed coverage from any machine learning model?

어떤 머신러닝 모형에서든 포함률이 보장되는 예측구간을 어떻게 얻나요?

Conformal prediction converts any model's predictions into intervals or sets that contain the true value at a chosen rate, such as 90 percent, using residuals on held-out calibration data. It fits when calibrated uncertainty is needed for black-box models, provided new data are exchangeable with the calibration data.

When to use Conformal Prediction

• A trained regression or classification model whose predictions need calibrated uncertainty
• Black-box models such as boosting or neural networks without native intervals
• Enough data to hold out a calibration set

When to use something else

• Strong distribution shift or time-ordered data that violate exchangeability; use adaptive conformal or time-series forecast intervals
• Parameter inference rather than prediction; use confidence intervals from a statistical model

Assumptions and requirements

• Exchangeable (for example i.i.d.) observations
• A held-out calibration set or cross-conformal scheme
• Coverage is marginal, not conditional on each covariate value

What the analysis reports

• Prediction intervals or prediction sets
• Empirical coverage on test data
• Average interval width or set size
• Nonconformity score quantile

How to report it

Split conformal prediction intervals achieved an empirical coverage of [value]% at a nominal level of [value]%, with an average width of [value].

Related methods

• Quantile Regression Analysis
• Bootstrap Prediction Intervals
• Conformalized Quantile Regression

Run Conformal Prediction on Quorant