Enhancing Machine Learning Performance Through Quantile Binning for Resource Forecasting
Bibliographic record
Abstract
Accurate resource yield prediction is critical for military logistics, planning, and operational readiness, yet remains challenging due to numerous influencing factors such as environmental conditions, resource quality, and logistical constraints. This study examines the effectiveness of quantile-based data binning on classical machine learning algorithms in predicting resource yields pertinent to military applications. Furthermore, the effectiveness of Backpropagation Artificial Neural Networks (BP-ANN) and Naive Bayes classifiers with regression models such as K-Nearest Neighbors (KNN), Linear Regression, and Multi-Layer Perceptron Regressors (MLPRegressor) are compared using a robust dataset representative of global resource metrics. The results indicate that binning continuous data into quartiles substantially enhances model accuracy, precision, recall, and computational efficiency. In particular, the binned data enables the BP-ANN to achieve an accuracy of approximately 90.4%, with regression models such as KNN and MLPRegressor outperforming this benchmark by attaining accuracies exceeding 93%. Additionally, binning drastically reduced hyperparameter tuning duration from around 149 minutes to less than 10 minutes, underscoring its computational efficiency advantage. Overall, this research demonstrates that quantile-based data binning is a valuable preprocessing technique that improves predictive accuracy, reduces computational cost, and enhances the reliability of classical machine learning models for military resource forecasting.
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How this classification was reachedexpand
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".