High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) Algorithm in Classifying Height Indicators Through Social-life and Well-being Factors
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Bibliographic record
Abstract
Introduction: High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) algorithm can incorporate interaction terms and combine with existing techniques for feature selection. Simulation studies have validated the ability of HDSI-BO to select true features and consequently, improve prediction accuracy compared to standard algorithms. Our goal is to assess the applicability of HDSI-BO in combining different techniques and measure its predictive performance in a real data study of predicting height indicators by social-life and well-being factors.
 Methods: HDSI-BO was combined with logistic regression, ridge regression, LASSO, adaptive LASSO, and elastic net. Two-way interaction terms were considered. Hyperparameters used in HDSI-BO were optimized through genetic algorithms with five-fold cross-validation. To measure the performance of feature selection, we fitted final models by logistic regression based on the sets of selected features and used the model’s AUC as a measure. 30 trials were repeated to generate a range of the number of selected features and a 95% confidence interval for AUC.
 Results: When combined with all of the above methods, HDSI-BO methods achieved higher final AUC values both in terms of mean and confidence interval. In addition, HDSI-BO methods effectively narrowed down the sets of selected features and interaction terms compared with standard methods.
 Conclusion: The HDSI-BO algorithm combines well with multiple standard methods and has comparable or better predictive performance compared with the standard methods. The computational and time complexity of HDSI-BO is higher but still acceptable. Considering AUC as the single metric cannot comprehensively measure the feature selection performance. More effective metrics of performance should be explored for future work.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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