Profiling waitlisted incoming students for future delinquency with an ensemble of statistical machine learning algorithms

Given a dataset \(\mathcal{R}=\{R_1, R_2, \dots, R_r\}\) of \(r\)~records of waitlisted incoming freshman students (WIFS), where for any \(i=1, 2, \dots, r\), \(R_i\) is a \((m+1)\)--tuple \((O_i, P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)})\), \(O_i\) is any one in a set \(\mathcal{O}=\{O_1, O_2, \dots, O_o\}\) of \(o\)~classes, and \(P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)}\) are \(m\)~potential predictors for~\(O_i\). Our purpose is to find a statistical machine learning algorithm (SMLA) \(\mathbb{A}\) such that \(V_i=\mathbb{A}(P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)})\), where \(V_i\) is a predicted class by~\(\mathbb{A}\) that was developed using \(n\le m\) correct number of predictors for \(O\in\mathcal{O}\), and \(\mathbb{A}\)~is the best algorithm such that the metric \(v^{-1}\sum_{i=1}^v |O_i - V_i|\) is minimum across \(v<r\)~records in the validation set \(\mathcal{V}\subset\mathcal{R}\). Our problem is to find the subset \(\{P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(n)}\}\) and to train \(\mathbb{A}\)~using \(t<r\) records from the training set \(\mathcal{T}\subset\mathcal{R}\), such that \(\mathcal{T}\cap\mathcal{V}=\emptyset\), so that \(\mathbb{A}\)~can predict whether a WIFS trying to enter an undergraduate program at UPLB will incur at least a ``delinquency'' once the student is accepted into the program. The \(\mathbb{A}\)~can be a useful decision-support tool for UPLB deans and college secretaries in deciding whether a WIFS will be accepted into the program or not.