Bibliographic record
Abstract
This research attempts to evaluate how various classical machine learning classifiers perform in predicting whether a loan application will be approved or rejected. A dataset of 4,269 observations and 13 features containing applicant's personal details, financial standing, and credit scores was utilized. The data preprocessing steps include categorical variable encoding, power transformation to handle skewness, and outlier analysis. Features are selected through statistical methods using ANOVA F-test, Chi-Square, Mutual Information, and Kendall correlation, which identified CIBIL score, loan term, income, and asset values as highly relevant predictors. To handle class imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied, resulting in a more balanced dataset for training. Multiple machine learning models were tested, including Logistic Regression, Support Vector Classifier (SVC), Decision Trees, Random Forests, K-Nearest Neighbors (KNN), and boosting method like XGBoost. The results showed that boosting method XGBoost worked best, achieving a test accuracy of 98.24% which is better than the other models. The results also show that using ensemble learning, along with good data preparation and balancing, is a strong approach for predicting loan approvals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".