Detection of Interacting Variables for Generalized Linear Models via Neural Networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909616151986176 |
|---|---|
| author | Havrylenko, Yevhen Heger, Julia |
| author_facet | Havrylenko, Yevhen Heger, Julia |
| contents | The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and often relies on visual performance indicators. Therefore, we present an approach to automating the process of finding interactions that should be added to GLMs to improve their predictive power. Our approach relies on neural networks and a model-specific interaction detection method, which is computationally faster than the traditionally used methods like Friedman H-Statistic or SHAP values. In numerical studies, we provide the results of our approach on artificially generated data as well as open-source data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_08030 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Detection of Interacting Variables for Generalized Linear Models via Neural Networks Havrylenko, Yevhen Heger, Julia Machine Learning Applications 62P05 The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and often relies on visual performance indicators. Therefore, we present an approach to automating the process of finding interactions that should be added to GLMs to improve their predictive power. Our approach relies on neural networks and a model-specific interaction detection method, which is computationally faster than the traditionally used methods like Friedman H-Statistic or SHAP values. In numerical studies, we provide the results of our approach on artificially generated data as well as open-source data. |
| title | Detection of Interacting Variables for Generalized Linear Models via Neural Networks |
| topic | Machine Learning Applications 62P05 |
| url | https://arxiv.org/abs/2209.08030 |