Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers

TL;DR

This paper introduces a probability transducer that converts classifier outputs into reliable probabilities, enabling optimal decision-making in critical applications like medicine and drug discovery, especially with imbalanced datasets.

Contribution

It proposes a computationally efficient method to derive class probabilities from classifier outputs, facilitating decision-theoretic optimization in complex, real-world problems.

Findings

Transducer improves decision accuracy in drug discovery tasks.

Method achieves near-optimal results across various utility settings.

Provides uncertainty quantification and supports biased dataset scenarios.

Abstract

In fields such as medicine and drug discovery, the ultimate goal of a classification is not to guess a class, but to choose the optimal course of action among a set of possible ones, usually not in one-one correspondence with the set of classes. This decision-theoretic problem requires sensible probabilities for the classes. Probabilities conditional on the features are computationally almost impossible to find in many important cases. The main idea of the present work is to calculate probabilities conditional not on the features, but on the trained classifier's output. This calculation is cheap, needs to be made only once, and provides an output-to-probability "transducer" that can be applied to all future outputs of the classifier. In conjunction with problem-dependent utilities, the probabilities of the transducer allow us to find the optimal choice among the classes or among a set of more general decisions, by means of expected-utility maximization. This idea is demonstrated in a simplified drug-discovery problem with a highly imbalanced dataset. The transducer and utility maximization together always lead to improved results, sometimes close to theoretical maximum, for all sets of problem-dependent utilities. The one-time-only calculation of the transducer also provides, automatically: (i) a quantification of the uncertainty about the transducer itself; (ii) the expected utility of the augmented algorithm (including its uncertainty), which can be used for algorithm selection; (iii) the possibility of using the algorithm in a "generative mode", useful if the training dataset is biased.