Comparing the Performance of Different Data Mining Techniques in Evaluating Loan Applications
Bibliographic record
Abstract
This study compares the performance of various data mining classifiers in order to find out which classifiers should be used for predicting whether a loan application will be approved or rejected. The study also tries to find the data mining classifiers which have the best performance in predicting whether an approved loan applicant will eventually default on his/her loan or not. The study was performed using a sample of 971 loan applicants. The results indicated that the best data mining classifier for predicting whether a loan applicant will be approved or rejected is LAD Tree, followed by Rotation Forest, Logit Boost, Random Forest, and AD Tree. It was also found that the best classifier for predicting whether an approved applicant will default on his/her loan is Bagging, followed by Simple Cart, J 48, J 48 graft, END, Class Balance ND, Data Near Balance ND, ND, and Ordinal Class Classifier.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".