Predicting purchase probability of retail items using an ensemble learning approach and historical data
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
Planning for stocking and supply chain management of retail markets is critical to ensure the high availability of retail items while reducing the risks of oversupply and overstocking. The ability to predict purchase probability of retail items accurately and efficiently is critical to enable such optimized supply chain management. In this paper, we use historical purchase data, carry out pre-processing, analysis, as well as build an ensemble learning-based model to efficiently predict purchase probability of retail items. The proposed ensemble learning model is composed of different segments utilizing Random Forests, Convolution Neural Networks, Extreme Gradient Boosting (XGBoost), and voting mechanism. Detailed evaluation of the proposed solution was carried out by analyzing accuracy, precision, F1 score, sensitivity, specificity, and more. The evaluation further included efficiency analysis, complexity analysis. The proposed solution performed better than the existing solutions, as shown in the evaluation.
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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 | 0.000 | 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) | 0.000 | 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