CON-FOLD -- Explainable Machine Learning with Confidence

TL;DR

CON-FOLD enhances explainable machine learning by providing confidence scores for rules, enabling better trust and interpretability, with efficient pruning and integration of prior knowledge demonstrated on benchmark and real-world datasets.

Contribution

It introduces a confidence scoring mechanism and pruning algorithm for FOLD-RM, along with the ability to incorporate prior knowledge, advancing explainability and reliability in rule-based models.

Findings

Effective confidence scoring with the Inverse Brier Score

Improved rule pruning to prevent overfitting

Successful application to real-world educational data

Abstract

FOLD-RM is an explainable machine learning classification algorithm that uses training data to create a set of classification rules. In this paper we introduce CON-FOLD which extends FOLD-RM in several ways. CON-FOLD assigns probability-based confidence scores to rules learned for a classification task. This allows users to know how confident they should be in a prediction made by the model. We present a confidence-based pruning algorithm that uses the unique structure of FOLD-RM rules to efficiently prune rules and prevent overfitting. Furthermore, CON-FOLD enables the user to provide pre-existing knowledge in the form of logic program rules that are either (fixed) background knowledge or (modifiable) initial rule candidates. The paper describes our method in detail and reports on practical experiments. We demonstrate the performance of the algorithm on benchmark datasets from the UCI Machine Learning Repository. For that, we introduce a new metric, Inverse Brier Score, to evaluate the accuracy of the produced confidence scores. Finally we apply this extension to a real world example that requires explainability: marking of student responses to a short answer question from the Australian Physics Olympiad.