Finding Dominant Factor That Affects Crude Birth Rates in Japanese Prefectures

Y. Shirota, K. Yamaguchi
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Abstract

We conduct a regression to find a dominant factor that affects crude birth rates in Japan by prefectures. As the traditional regression method, a linear multiple regression is widely used. However, higher accuracy methods with machine learning algorithms have been developed. To find the dominant factor, we use eXtreme Gradient Boosting (XGBoost) and Random Forest which are the decision tree based machine learning algorithms. The results show better accuracies, compared with the traditional linear multiple one. Then, the XGBoost shows that the most dominant factor is the number of marriages, and the second one is the migration rate to the prefecture.
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发现影响日本各县粗出生率的主要因素
我们进行了回归,以找到影响日本各县粗出生率的主导因素。作为传统的回归方法,线性多元回归得到了广泛的应用。然而,使用机器学习算法的更高精度的方法已经被开发出来。为了找到主导因素,我们使用了极端梯度增强(XGBoost)和随机森林,这是基于决策树的机器学习算法。结果表明,该方法与传统的线性多重方法相比,具有更好的精度。然后,XGBoost显示,最主要的因素是婚姻数量,其次是向该地区的移民率。
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