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Full-Text Articles in Physical Sciences and Mathematics
Logistic Ensemble Models, Bob Vanderheyden, Jennifer L. Priestley
Logistic Ensemble Models, Bob Vanderheyden, Jennifer L. Priestley
Jennifer L. Priestley
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness must be interpretable and “rational” (e.g., improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good performance with minimal analyst intervention, so they are well suited to a high volume analytic environment but the majority are “black box” tools that provide very limited insight or interpretability into key drivers of model performance or predicted model output values. This paper presents a methodology that blends one of the most popular predictive statistical modeling methods with …
Binary Classification On Past Due Of Service Accounts Using Logistic Regression And Decision Tree, Yan Wang, Jennifer L. Priestley
Binary Classification On Past Due Of Service Accounts Using Logistic Regression And Decision Tree, Yan Wang, Jennifer L. Priestley
Jennifer L. Priestley
This paper aims at predicting businesses’ past due in service accounts as well as determining the variables that impact the likelihood of repayment. Two binary classification approaches, logistic regression and the decision tree, were conducted and compared. Both approaches have very good performances with respect to the accuracy. However, the decision tree only uses 10 predictors and reaches an accuracy of 96.69% on the validation set while logistic regression includes 14 predictors and reaches an accuracy of 94.58%. Due to the large concern of false negatives in financial industry, the decision tree technique is a better option than logistic regression …