使用机器学习算法进行住房条件预测的大规模建模
Kyusik Kim1,2, Tisha Holmes3, Emily Powell4
1Florida State University, Department of Geography, Tallahassee, FL, USA. kkim84@kennesaw.edu.
Scientific data
|March 11, 2026
概括
这项研究开发了一个机器学习模型来预测国家住房条件,解决数据的局限性. 选择CatBoost的原因是它对超的抗性,为空间分析提供了宝贵的资源.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 住房价格预测是常见的,但大规模的住房状况预测受到数据可用性的限制.
- 现有研究尚未充分探索美国各地住房质量的空间差异.
- 了解住房条件对于各种社会应用至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于在全国范围内预测住房条件.
- 克服大规模住房质量评估中的数据限制.
- 创建一个全面的数据集,用于对住房条件的空间分析.
主要方法:
- 综合物业级数据 (沃伦集团) 与美国人口普查局的社区数据.
- 训练并比较了三个梯度增强算法:CatBoost,LightGBM和XGBoost.
- 选择了CatBoost作为最佳型号,因为它对过度装配的优越抵抗力.
主要成果:
- CatBoost模型表现出对住房条件的强有力的预测性能.
- 预测被聚合到人口普查区,邮政编码表格区和六角网格进行空间分析.
- 为全国范围的住房质量分析创建了一个全面的数据集.
结论:
- 开发的机器学习模型有效地预测了国家住房条件.
- 由此产生的数据集是分析住房质量的地理位置的宝贵资源.
- 应用包括城市规划,灾害管理,社区弹性和公共卫生.
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