Estimating non-overfitted convex production technologies: A stochastic machine learning approach
Bibliographic record
Abstract
• A new approach to solve overfitting in convex technologies using machine learning. • Stochastic Gradient Boosting estimates technologies with shape constraints. • The approach complements DEA by enhancing generalization. • Simulation results show better performance compared to traditional DEA models. • An empirical example demonstrates its practical use. Overfitting is a classical statistical issue that occurs when a model fits a particular observed data sample too closely, potentially limiting its generalizability. While Data Envelopment Analysis (DEA) is a powerful non-parametric method for assessing the relative efficiency of decision-making units (DMUs), its reliance on the minimal extrapolation principle can lead to concerns about overfitting, particularly when the goal extends beyond evaluating the specific DMUs in the sample to making broader inferences. In this paper, we propose an adaptation of Stochastic Gradient Boosting to estimate production possibility sets that mitigate overfitting while satisfying shape constraints such as convexity and free disposability. Our approach is not intended to replace DEA but to complement it, offering an additional tool for scenarios where generalization is important. Through simulation experiments, we demonstrate that the proposed method performs well compared to DEA, especially in high-dimensional settings. Furthermore, the new machine learning-based technique is compared to the Corrected Concave Non-parametric Least Squares (C 2 NLS), showing competitive performance. We also illustrate how the usual efficiency measures in DEA can be implemented under our approach. Finally, we provide an empirical example based on data from the Program for International Student Assessment (PISA) to demonstrate the applicability of the new method.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".