Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910656233472000 |
|---|---|
| author | Meng, Fanyu Larke, Jules Liu, Xin Kong, Zhaodan Chen, Xin Lemay, Danielle Tagkopoulos, Ilias |
| author_facet | Meng, Fanyu Larke, Jules Liu, Xin Kong, Zhaodan Chen, Xin Lemay, Danielle Tagkopoulos, Ilias |
| contents | Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-explored type of explanation is cohort explanation, which provides explanations to groups of instances with similar characteristics. Unlike traditional methods that focus on individual explanations or global model behavior, cohort explainability bridges the gap by providing unique insights at an intermediate granularity. We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores, aiming to generate concise descriptions of the clusters via tags. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation Meng, Fanyu Larke, Jules Liu, Xin Kong, Zhaodan Chen, Xin Lemay, Danielle Tagkopoulos, Ilias Machine Learning Artificial Intelligence Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-explored type of explanation is cohort explanation, which provides explanations to groups of instances with similar characteristics. Unlike traditional methods that focus on individual explanations or global model behavior, cohort explainability bridges the gap by providing unique insights at an intermediate granularity. We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores, aiming to generate concise descriptions of the clusters via tags. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge. |
| title | Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.14082 |