Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article compares the predictive capabilities of six models, namely, linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), XGBoost, random forest (RF), and deep neural network (DNN), to predict the default behavior of credit card holders in Taiwan using data from the UCI machine learning database. The Python programming language was used for data analysis. Statistical methods were compared with machine learning algorithms using the confusion matrix measured in metric terms of prediction accuracy, sensitivity, specificity, precision, G-mean, F1 score, ROC, and AUC. The dataset contained 30,000 credit card users’ information, with 6636 default observations and 23,364 nondefault cases. The study results found that modern machine learning methods outperformed traditional statistical methods in terms of predictive performance measured by the F1 score, G-mean, and AUC. Traditional methods like logistic regression were marginally better than linear discriminant analysis and support vector machines in terms of the predictive performance measured by the area under the receiver operating characteristic curve. In the modern machine learning methods, deep neural network was better in the predictive performance metrics when compared with XGBoost and random forest methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it