Abstract
This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.
| Original language | English |
|---|---|
| Publication status | Published - 1 Sept 2023 |
| Event | 2023 American Political Science Association Annual Meeting & Exhibition - Los Angeles, United States Duration: 31 Aug 2023 → 3 Sept 2023 https://connect.apsanet.org/apsa2023/ |
Conference
| Conference | 2023 American Political Science Association Annual Meeting & Exhibition |
|---|---|
| Abbreviated title | APSA 2023 |
| Country/Territory | United States |
| City | Los Angeles |
| Period | 31/08/23 → 3/09/23 |
| Internet address |