One key issue is information leakage, and we discuss its definition, influence, detection and avoidance. We consider leakage to be the silent killer of many predictive modeling projects, and we demonstrate its impact on the competitions, and discuss the challenges in addressing it in the real-life projects. Other challenges include framing real-life modeling objectives into predictive modeling, and usefully applying relational learning concepts when modeling “real-life” complex, relational datasets.
Tags#lak12 accountability atonement big data blogging boredom career causation cfa charter schools connectivism cultural values culture data mining data science data scientist data scientists depression detachment difficulty dissertation dps efa experience experiencing self experiments factor analysis for-profit schools gdp government growth mixture modeling happiness health hero's journey higher education hlm intelligence intrinsic motivation Joseph Campbell kahneman latent growth curve modeling learning analytics lms machine learning math measurement measurement invariance mediation memory monomyth Montaigne networks normal distribution novelty-seeking productivity psychology publicy quasi-experiments rating scales regression reliability remembering self research design response styles rhetoric scatterplots self-determination theory spring storytelling structural equation modeling timss validity waste well-being world values survey
Search this site