Mean Square Error Based Method for Parameter Ranking and Selection To Obtain Accurate Predictions at Specified Operating Conditions
Bibliographic record
Abstract
A mean-squared-error-based forward selection methodology is proposed for simultaneous parameter ranking and selection based on the critical ratio r CCW [Eghtesadi, Z.; Wu, S.; McAuley, K. B. Ind. Eng. Chem. Res. 2013, 52, 12297]. This new technique employs information in the available data set and the operating region of interest to determine the best model with the lowest mean square prediction error. This technique involves relatively simple computations and avoids overfitting of noisy data. This new approach is valuable when data available for parameter estimation arise from correlated experimental designs that make accurate estimation of all the parameters difficult. It is particularly beneficial when the predictions are desired in an operating region that is different from where the data are already available. Monte Carlo simulations of a linear regression example and a nonlinear case study are used to illustrate the effectiveness of the proposed method, demonstrating that results from simulated data agree with theoretical results.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".