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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.11874 |
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| _version_ | 1866910414467497984 |
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| author | Nakano, Shou Liu, Yang |
| author_facet | Nakano, Shou Liu, Yang |
| contents | This paper focuses on explaining changes over time in globally-sourced, annual temporal data, with the specific objective of identifying pivotal factors that contribute to these temporal shifts. Leveraging such analytical frameworks can yield transformative impacts, including the informed refinement of public policy and the identification of key drivers affecting a country's economic evolution. We employ Local Interpretable Model-agnostic Explanations (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate time-series datasets apt for LIME's input requirements. Our methodology's efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These 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, a state-of-the-art technique proficient in feature importance detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data Nakano, Shou Liu, Yang Machine Learning Artificial Intelligence This paper focuses on explaining changes over time in globally-sourced, annual temporal data, with the specific objective of identifying pivotal factors that contribute to these temporal shifts. Leveraging such analytical frameworks can yield transformative impacts, including the informed refinement of public policy and the identification of key drivers affecting a country's economic evolution. We employ Local Interpretable Model-agnostic Explanations (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate time-series datasets apt for LIME's input requirements. Our methodology's efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These 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, a state-of-the-art technique proficient in feature importance detection. |
| title | Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.11874 |