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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 …
Application Of Isotonic Regression In Predicting Business Risk Scores, Linh T. Le, Jennifer L. Priestley
Application Of Isotonic Regression In Predicting Business Risk Scores, Linh T. Le, Jennifer L. Priestley
Jennifer L. Priestley
An isotonic regression model fits an isotonic function of the explanatory variables to estimate the expectation of the response variable. In other words, as the function increases, the estimated expectation of the response must be non-decreasing. With this characteristic, isotonic regression could be a suitable option to analyze and predict business risk scores. A current challenge of isotonic regression is the decrease of performance when the model is fitted in a large data set e.g. more than four or five dimensions. This paper attempts to apply isotonic regression models into prediction of business risk scores using a large data set …
A Comparison Of Decision Tree With Logistic Regression Model For Prediction Of Worst Non-Financial Payment Status In Commercial Credit, Jessica M. Rudd Mph, Gstat, Jennifer L. Priestley
A Comparison Of Decision Tree With Logistic Regression Model For Prediction Of Worst Non-Financial Payment Status In Commercial Credit, Jessica M. Rudd Mph, Gstat, Jennifer L. Priestley
Jennifer L. Priestley
Credit risk prediction is an important problem in the financial services domain. While machine learning techniques such as Support Vector Machines and Neural Networks have been used for improved predictive modeling, the outcomes of such models are not readily explainable and, therefore, difficult to apply within financial regulations. In contrast, Decision Trees are easy to explain, and provide an easy to interpret visualization of model decisions. The aim of this paper is to predict worst non-financial payment status among businesses, and evaluate decision tree model performance against traditional Logistic Regression model for this task. The dataset for analysis is provided …
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 …
An Analysis Of Accuracy Using Logistic Regression And Time Series, Edwin Baidoo, Jennifer L. Priestley
An Analysis Of Accuracy Using Logistic Regression And Time Series, Edwin Baidoo, Jennifer L. Priestley
Jennifer L. Priestley
This paper analyzes the accuracy rates for logistic regression and time series models. It also examines a relatively new performance index that takes into consideration the business assumptions of credit markets. Although prior research has focused on evaluation metrics, such as AUC and Gini index, this new measure has a more intuitive interpretation for various managers and decision makers and can be applied to both Logistic and Time Series models.