Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge
Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge
复制标题
脆弱家庭挑战中平均绩点、毅力和裁员的获胜模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Abdullah Almaatouq
中科院分区：
文献类型：
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作者：
Daniel E. Rigobon;E. Jahani;Yoshihiko Suhara;Khaled Al;Abdulaziz Alghunaim;A. Pentland;Abdullah Almaatouq
在这篇文章中，作者讨论和分析了他们应对脆弱家庭挑战的方法。这些数据包括12，000多个关于儿童及其父母，学校和从出生到9岁的整体环境的特征（协变量）。作者的模块化和协作方法并行化了预测任务，主要依赖于现有的数据科学技术，包括（1）数据预处理：消除低方差特征，缺失数据的填补和复合特征的构建;（2）通过单变量互信息和非零最小绝对收缩和选择算子系数的提取进行特征选择;（3）三种机器学习模型：随机森林、弹性网络和梯度提升树;最后（4）根据性能进行预测聚合。表现最好的提交产生了三个结果的样本外预测：平均成绩，毅力和裁员。然而，预测结果最多比预测每个结果的训练数据平均值的基线好20%。
In this article, the authors discuss and analyze their approach to the Fragile Families Challenge. The data consisted of more than 12,000 features (covariates) about the children and their parents, schools, and overall environments from birth to age 9. The authors’ modular and collaborative approach parallelized prediction tasks and relied primarily on existing data science techniques, including (1) data preprocessing: elimination of low variance features, imputation of missing data, and construction of composite features; (2) feature selection through univariate mutual information and extraction of nonzero least absolute shrinkage and selection operator coefficients; (3) three machine learning models: random forest, elastic net, and gradient-boosted trees; and finally (4) prediction aggregation according to performance. The top-performing submissions produced winning out-of-sample predictions for three outcomes: grade point average, grit, and layoff. However, predictions were at most 20 percent better than a baseline that predicted the mean value of the training data for each outcome.