Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data
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DOI：
10.1093/pan/mpv024
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发表时间：
2016-12-01
影响因子：
5.4
通讯作者：
Kocher, Matthew
中科院分区：
文献类型：
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作者：
Muchlinski, David;Siroky, David;Kocher, Matthew
最常用的内战爆发的统计模型无法正确预测这种罕见事件的样本外数据的大多数发生。用于分析二进制数据的统计方法，例如逻辑回归，即使在其罕见事件和正则化形式中，预测性能也很差。我们将随机森林的性能与三种版本的逻辑回归（经典逻辑回归，Firth罕见事件逻辑回归和L-1正则化逻辑回归）进行了比较，发现算法方法在样本外数据中提供了比任何逻辑回归模型更准确的内战爆发预测。本文讨论了这些结果，以及随机森林等算法统计方法如何有助于更准确地预测冲突数据中的罕见事件。
The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L-1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.