# Reliable Probabilistic Classification with Neural Networks

arXiv: https://arxiv.org/abs/2312.09912 · HTML: https://mlsanity.com/paper/2312.09912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Regression Conformal Prediction with Nearest Neighbours](https://mlsanity.com/paper/1401.3880/) (2014)

## Built on by (most cited first, 1 of 1)

- [Vesicoureteral Reflux Detection with Reliable Probabilistic Outputs](https://mlsanity.com/paper/2312.11355/) (2023)

## Abstract (arXiv)

Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possible class of the problem at hand. This paper proposes five VP methods based on Neural Networks (NNs), which is one of the most widely used machine learning techniques. The proposed methods are evaluated experimentally on four benchmark datasets and the obtained results demonstrate the empirical well-calibratedness of their outputs and their superiority over the outputs of the traditional NN classifier.