A Procedure to Improve Binary Classification Models and Categorize Features: The Case of the Distribution of Three Mosquito Species in Morocco
Bibliographic record
Abstract
Modeling biological datasets represents an essential step in processing and exploiting biological information.Selecting features and improving modeling quality are critical in building a high-performance predictive model.In this article, we have presented and applied a novel approach to select features and to improve the modeling quality using the presence/absence data of three mosquito species in Morocco.This approach uses a recursive search of feature subsets conditioned on improving the modeling quality compared to an initially chosen solution.It has led to a significant improvement in the modeling quality compared to another study carried out on the same dataset, where the accuracy of the models improved with a range varying between 0.062 and 0.198.The relevance of this approach also extends to the search for solutions that achieve the same performance with different subsets, known as multiple solutions.These solutions demonstrate that various combinations of explanatory features can explain the target feature, leading to categorizing them according to their impact on the modeling.This work has provided a good explanation of the distribution of mosquito species thanks to the improved modeling quality, opening up the possibility of having relevant solutions and discovering new explanatory modes for the features.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".