Summary of Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data, by Shou Nakano and Yang Liu
Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data
by Shou Nakano, Yang Liu
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper aims to identify pivotal factors contributing to temporal shifts in globally-sourced annual data, with potential applications in refining public policy and understanding economic evolution. By employing Local Interpretable Model-agnostic Explanations (LIME), the study sheds light on national happiness indices, economic freedom, and population metrics across various timeframes. The authors acknowledge missing values and develop three imputation approaches to generate robust multivariate time-series datasets suitable for LIME’s input requirements. Empirical evaluations include comparative analyses against random feature selection, correlation with real-world events as elucidated by LIME, and validation through Individual Conditional Expectation (ICE) plots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how things change over time using global data from every year. The goal is to find important factors that cause these changes. The study uses a special tool called Local Interpretable Model-agnostic Explanations (LIME) to understand national happiness, economic freedom, and population growth. To make the data work with LIME, the authors create three ways to fill in missing values. They test their method by comparing it to other methods and checking how well it works. |
Keywords
» Artificial intelligence » Feature selection » Time series
