Summary of Automated Machine Learning in Insurance, by Panyi Dong et al.
Automated Machine Learning in Insurance
by Panyi Dong, Zhiyu Quan
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper introduces an Automated Machine Learning (AutoML) workflow specifically designed for the insurance industry. The proposed AutoML system allows users without domain knowledge or prior experience to achieve robust and effortless ML deployment by writing only a few lines of code. The workflow is tailored for the insurance application, featuring balancing steps in data preprocessing, ensemble pipelines, and customized loss functions to address the unique challenges of insurance datasets, including imbalanced data. The system aims to automate the full life-cycle of ML tasks, providing state-of-the-art models without human intervention or supervision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has become popular in actuarial research and insurance applications. But most ML tasks rely heavily on data preprocessing, model selection, and hyperparameter optimization. This is time-consuming and requires domain knowledge, experience, and manual labor. Automated machine learning aims to automate the full life-cycle of ML tasks, providing state-of-the-art models without human intervention or supervision. The paper introduces an AutoML workflow that allows users to achieve robust and effortless ML deployment by writing only a few lines of code. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization
