  This study is motivated by the magnitude of the problem of Louisiana high
school dropout and its negative impacts on individual and public well-being.
Our goal is to predict students who are at risk of high school dropout, by
examining Louisiana administrative dataset. Due to the imbalanced nature of the
dataset, imbalanced learning techniques including resampling, case weighting,
and cost-sensitive learning have been applied to enhance the prediction
performance on the rare class. Performance metrics used in this study are
F-measure, recall and precision of the rare class. We compare the performance
of several machine learning algorithms such as neural networks, decision trees
and bagging trees in combination with the imbalanced learning approaches using
an administrative dataset of size of 366k+ from Louisiana Department of
Education. Experiments show that application of imbalanced learning methods
produces good results on recall but decreases precision, whereas base
classifiers without regard of imbalanced data handling gives better precision
but poor recall. Overall application of imbalanced learning techniques is
beneficial, yet more studies are desired to improve precision.
