Summary of Improving the Sampling Strategy in Kernelshap, by Lars Henry Berge Olsen and Martin Jullum
Improving the Sampling Strategy in KernelSHAP
by Lars Henry Berge Olsen, Martin Jullum
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The KernelSHAP framework is an approximation technique for calculating Shapley values, a popular model-agnostic explanation framework for complex machine learning models. The original Shapley value computation is computationally expensive due to estimating exponential conditional expectations. This paper proposes three novel contributions: a stabilizing technique to reduce variance in the current state-of-the-art strategy, a weighing scheme that corrects Shapley kernel weights based on sampled subsets, and a straightforward strategy that integrates important subsets with corrected Shapley kernel weights. The results demonstrate that these new approximation strategies significantly enhance the accuracy of approximated Shapley value explanations, making them more reliable in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shapley values help us understand how machine learning models work by explaining predictions. This is important because complex models can be hard to interpret. To make this process faster and better, researchers created a new way to approximate Shapley values using samples from the data. The new method reduces mistakes and makes it more reliable for real-world use. |
Keywords
* Artificial intelligence * Machine learning
