OptCatB: Optuna Hyperparameter Optimization Model to Forecast the Educational Proficiency of Immigrant Students based on CatBoost Regression
Bibliographic record
Abstract
A person's poor educational performance or academic success would hinder the struggle against poverty that plagues humanity, particularly for children who are close to completing high school. This study examines the PISA dataset containing immigrant student information from nations like the UAE, New Zealand, Canada, Qatar, Spain, and Australia. As a result, they place a high priority on analyzing the performance of immigrant students to provide them with a high-quality education. Based on the data analysis and interpretation of the findings, factors like early arrival, late arrival, wealth factors, family circumstances, and a multitude of other socioeconomic factors have an influence on the performance of students in reading, math, and science scores. The proposed OptCatB model makes predictions regarding the academic success of immigrant students by applying an optimized CatBoost regressor by keeping reading, math, and science as target variables. We trained the model using the optimized parameters after tuning the hyperparameters of the CatBoost algorithm by using a hyperparameter optimization technique termed Optuna. The OptCatB model outperformed compared to the other selected regression models with RMSE of 54.231, MAE of 43.104, MAPE of 9.931 and RSE of 0.54.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".