A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data

Abstract

Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a Synthetic Penalized Logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients can get stabilized in the recursive procedure, and (iii) reduces the risk of overfitting. We consider a mortgage lending case study using publicly available data to illustrate our method. Results show that errors are smaller in many extreme prediction scores, outperforming a number of existing methods. Our interpretations are consistent with results obtained using a classic econometric model.

RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

“…Similar performance is obtained between the Weighted Logistic Regression (WLR) [26], Penalized Logistic regression for complex surveys (PLR), with the two weighting mechanisms PSWa and PSWb [9], and SyntheticPL (Synthetic Penalized Logitboost) [56]. Both WLR and PLR with PSWa provide exactly the same result because the PLR incorporates the sampling design, as well as a resampling correction.…”

“…Refs. [9,26,55,56] proposed weighting mechanisms for parametric and non-parametric models to improve the predictive performance of imbalanced and rare data.…”

“…Abbreviations: WLR (Weighted Logistic Regression) [26], PLR (Penalized Logistic regression for complex surveys), with two weighting mechanisms PSWa and PSWb [9]. SyntheticPL (Synthetic Penalized Logitboost) [56], WeiLogRFL (Weighted Logistic) of [10]. The results are presented for observations that correspond to policy holders who did not suffer an accident (Y = 0).…”