Summary of Feature Importance and Explainability in Quantum Machine Learning, by Luke Power and Krishnendu Guha
Feature Importance and Explainability in Quantum Machine Learning
by Luke Power, Krishnendu Guha
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantum Algebra (math.QA); Machine Learning (stat.ML)
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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 This paper explores the application of Quantum Machine Learning (QML) to improve the transparency and trust in predictions made by Machine Learning (ML) models. By leveraging quantum mechanical phenomena like superposition, QML can provide valuable insights into feature importance and model explanations, particularly crucial in high-stakes domains such as healthcare and finance. The study compares classical ML algorithms with their hybrid quantum counterparts, implemented using IBM’s Qiskit platform. The results demonstrate that QML models can generate more accurate and interpretable predictions when applying permutation feature importance methods, alongside ALE and SHAP explainers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machine learning models more understandable. Right now, many models are like black boxes, making it hard to know why they make certain predictions. In important areas like healthcare and finance, this lack of transparency can be a problem. The researchers explore how using quantum computers can help improve the situation. They compare regular machine learning algorithms with new quantum-based ones, looking at how well each one explains its decisions. The results show that the quantum models can do a better job of providing insights into their predictions. |
Keywords
» Artificial intelligence » Machine learning
