Credit Card Approval Predictions Using Logistic Regression, Linear SVM and Naïve Bayes Classifier
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
With the huge growth of financial institution databases, the evaluation of credit issuance decisions begins to improve the decision-making methods of manual judgment and statistical analysis, which greatly improves the reliability and efficiency of credit issuance decisions. Machine learning algorithms, as one of the most important statistical tools, have witnessed increasing importance in supporting credit approval decisions. However, based on the different algorithms of each model and the selection of corresponding parameters in a given model, the prediction performances have varied among prediction models. To better evaluate the prediction performance of prevalent models and improve the model construction in the credit scoring process, this paper analyzes the prediction accuracy of multiple regression models and classifiers based on a predetermined performance criterion and proposes an optimal model with the highest prediction accuracy. The experimental models involved in the analysis include Logistic Regression, Linear Support Vector Classification (Linear SVC) and Naïve Bayes Classifier. According to the reported results, Linear SVC performed well in the analyzed model, reflecting the highest performance score of Balanced Accuracy (89.09%) and Accuracy Rate (88.48%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it