Build Prediction Sets with Split Conformal Classification

Sat, 05 Sep 2026 00:00:00 +0700

A classifier usually returns one label or a vector of scores. That is convenient when the application must choose one answer, but it hides an important distinction: some inputs strongly support one class, while others leave several classes plausible.

Conformal prediction provides a way to expose that ambiguity. For classification, it can return a prediction set containing one or more labels instead of forcing every input into a single choice. With an appropriate calibration procedure and statistical assumptions, the method can target a long-run coverage level such as 90%: roughly speaking, the true label should appear in the prediction set for at least that proportion of future examples.

Estimate Neural Network Uncertainty with Monte Carlo Dropout

Sat, 05 Sep 2026 00:00:00 +0700

A neural network can produce a confident-looking prediction even when the input is unlike the data it learned from. A single output such as 0.93 tells you what one forward pass predicts; by itself, it does not tell you how sensitive that prediction is to uncertainty in the learned model.

Monte Carlo dropout is a practical way to obtain an additional uncertainty signal from some neural networks that were trained with dropout. Instead of disabling dropout at inference time, you keep it active, run the same input through the network multiple times, and inspect how much the predictions vary.

Uncertainty on Nalar

Build Prediction Sets with Split Conformal Classification

Estimate Neural Network Uncertainty with Monte Carlo Dropout