  In this paper, we discuss and analyze our approach to the Fragile Families
Challenge. The challenge involved predicting six outcomes for 4,242 children
from disadvantaged families from around the United States. The data consisted
of over 12,000 features (covariates) about the children and their parents,
schools, and overall environments from birth to age 9. Our approach 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 non-zero LASSO 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: GPA, grit, and layoff. However, predictions
were at most 20% better than a baseline that predicted the mean value of the
training data of each outcome.
