THE LEAST EXPLANATION

Anxieties and Connecticut's Future Employment Profile

Connecticut's Economic Recovery: Prospects October 22, 2020

There seems to be a deep well of anxiety triggers in Connecticut. We hear concerned talk parsing and attempting to understand what are, or will be, the cognitive impairments afflicting an entire generation of our youth by quarantine-associated remote-learning schooling. We hear hand-wringing over the lost momentum that has befallen our young athletes, dancers and others who imperfectly sustain their physical conditioning and experience in the current climate of seasonal shutdowns and shuttered theaters and dance studios.

Model Drift in the Academic Admissions Process

Managing Change, March 15, 2022

Over the last few years, as a result of events endured, the machine learning models built for admissions and program evaluation purposes lost their predictive power — a condition known as model drifting. In this talk, I discuss the relevance of model drift in academe - especially the combined impact of COVID restrictions, social upheaval, and the demographic-shift on the leading causes of model drift: concept drift and covariate drift. Succinctly, the events drove changes in the stationarity of the target variable and the predictors. Formally, I frame the resulting situation as a One Class problem to rely on several well-known one-class algorithms: Support Vector Machines and Random Forests. We explain their use in reconstructing a true representative sample of our student body. Last, we use propensity matching to train a new admissions model on the reconstructed data set. Matching is required to adjust for well-known selection biases. Biases that may have also been impacted by recent events.