Summary of Con-fold — Explainable Machine Learning with Confidence, by Lachlan Mcginness and Peter Baumgartner
CON-FOLD – Explainable Machine Learning with Confidence
by Lachlan McGinness, Peter Baumgartner
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary FOLD-RM is an explainable machine learning algorithm for classification tasks, utilizing training data to generate rules. The paper introduces CON-FOLD, which extends FOLD-RM by assigning probability-based confidence scores to these rules. This allows users to gauge the reliability of predictions made by the model. A confidence-based pruning algorithm is also presented, leveraging the unique structure of FOLD-RM rules to efficiently prune rules and prevent overfitting. Additionally, CON-FOLD enables the incorporation of pre-existing knowledge in the form of logic program rules, which can be fixed or modified as initial rule candidates. The method is detailed, and practical experiments are reported on benchmark datasets from the UCI Machine Learning Repository. A new metric, Inverse Brier Score, is introduced to evaluate the accuracy of produced confidence scores. Finally, CON-FOLD is applied to a real-world example requiring explainability: marking student responses to an Australian Physics Olympiad short answer question. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make machine learning models more transparent and reliable. The method, called CON-FOLD, helps users understand how confident they should be in the predictions made by the model. It does this by giving each prediction a score showing how likely it is to be correct. The algorithm also lets users add their own knowledge to help improve the model’s performance. The authors tested their method on some common datasets and found that it works well. They even used it to grade student answers to a physics problem, which requires explainability. Overall, CON-FOLD makes machine learning models more trustworthy by giving them confidence scores. |
Keywords
» Artificial intelligence » Classification » Machine learning » Overfitting » Probability » Pruning
