Predicting Loan Default: A Comparative Analysis of Multiple Machine Learning Models
Bibliographic record
Abstract
Financial decision-making, particularly in loan approval, requires precise risk prediction. To enhance the prediction accuracy, this study utilizes various machine learning models, namely Logistic Regression, XGBoost, an Artificial Neural Network (ANN), and a hybrid XGBoost + Logistic Regression (XGB+LR). These models were selected based on their unique capacities to capture complex patterns and relationships within the data, thereby potentially improving the loan default prediction task. The training and validation of these models were performed on a meticulously prepared dataset, following crucial preprocessing steps such as one-hot encoding, feature selection, and scaling. To ensure the models' optimal performance, intensive hyperparameter tuning was conducted. The application of these techniques resulted in a robust set of models. Each model's performance was rigorously evaluated through established metrics, including the Area Under the ROC Curve (AUC) and Accuracy (ACC). Among these models, the XGBoost model demonstrated superior predictive power, achieving an AUC of 0.798 and an ACC of 0.861 on the validation set. A detailed feature importance analysis using the XGBoost model further revealed that Credit_Score and Loan_Amount were the primary factors impacting loan approval decisions. Despite slight overfitting observed in the models, the results confirm the potential of machine learning in improving financial decision-making processes. This study sets the foundation for future advancements, which may include the application of advanced regularization techniques, further hyperparameter optimization, and the inclusion of a broader feature set.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".