Summary of Tackling the Accuracy-interpretability Trade-off in a Hierarchy Of Machine Learning Models For the Prediction Of Extreme Heatwaves, by Alessandro Lovo et al.
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves
by Alessandro Lovo, Amaury Lancelin, Corentin Herbert, Freddy Bouchet
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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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 addresses the trade-off between performance and interpretability in machine learning (ML) models, with a focus on climate science applications. The authors present probabilistic forecasts of extreme heatwaves over France using a hierarchy of increasingly complex ML models, ranging from simple Gaussian Approximation (GA) to deep Convolutional Neural Networks (CNNs). They find that CNNs provide high accuracy but limited interpretability, even with state-of-the-art Explainable Artificial Intelligence (XAI) tools. In contrast, the ScatNet model achieves similar performance while providing greater transparency and identifying key scales and patterns in the data. The study highlights the importance of interpretability in ML models for climate science, demonstrating that simpler models can rival the performance of more complex ones while being easier to understand. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how machine learning (ML) can be used to predict extreme weather events like heatwaves. The authors tested different types of ML models to see which one works best for this task. They found that the most accurate model was a deep neural network, but it’s hard to understand why it gives certain predictions. On the other hand, a simpler model called ScatNet is easier to understand and still provides good results. This study shows how important it is to make ML models more understandable so we can trust what they’re telling us. |
Keywords
* Artificial intelligence * Machine learning * Neural network
