Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In the coming weeks, students around the nation will hear their names read aloud, walk across a platform and move their tassels to signify graduation from high school or college. After years of working toward receiving their diploma or degree, they are now off to start a new adventure in their lives. And, for others, there may be the apprehension of not knowing what is next. As a college president, here are five pieces of advice I have for graduating seniors.

-- This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public wellbeing. 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. Louisiana has maintained one of the highest school dropout rates in the US for many years. The Public Affairs Research Council of Louisiana (PAR, October 2011) estimates that one in six of every public high school students in the state drops out of school.