Unifying Variable Importance Scores from Different Machine Learning Models Using Simulated Annealing
Bibliographic record
Abstract
Each machine learning algorithm might generate different variable importance even though the identical loss function is used.The difference in the predictor rank order makes it difficult to interpret, so a single predictor rating is required.This paper proposes a method that combines predictor rating from machine learning using simulated annealing algorithms.Simulation and empirical data are used to apply the method and its evaluation.The simulation data contain as many as 24 predictors, 1000 observations, and 100 iterations.Four machine learning algorithms are used: random forest, XGBoost, neural network, and support vector machine.Then, four permutation importance variables were produced with 100 repetitions.Next, a simulated annealing algorithm generates a combined variable importance.This proposed method will be optimal if predictors are independent, and the number of predictors is more than ten.Then, the proposed method was applied to empirical data.Using the proposed method, the machine learning model needs only 14 predictors to reach the accuracy of 74.4% which is similar to the result if the algorithm involves all predictors.The proposed method is possible to be further developed and modified.The change could be employed in the objective value and the solution strategy within the simulated annealing procedure.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".