Feature Selection and Classification Performance: A Multi-Dataset Comparative Analysis Using Boruta Algorithm and Random Forest
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Bibliographic record
Abstract
Dimensionality reduction is crucial for managing high-dimensional datasets in machine learning, reducing complexity and overfitting.This study evaluates the efficiency of classification models without and with feature selection using the Boruta algorithm with Random Forest classifiers across three distinct datasets.Feature selection aims to improve model accuracy and interpretability by retaining only the most significant features.The three datasets were evaluated using full and reduced feature sets by comparing accuracy, precision, recall, and F1-score.Results show that feature selection significantly enhances model performance.For Dataset 1, accuracy improved by 1.06%, precision by 3.23%, recall by 3.46%, and F1-score by 3.36%.Dataset 2 saw increases in accuracy by 0.46%, precision by 2.36%, recall by 4.82%, and F1-score by 5.42%.Dataset 3 showed no significant changes, with both configurations yielding similar performance metrics.These findings confirm that the Boruta algorithm effectively enhances classification performance by reducing dataset dimensionality and retaining key features, especially in datasets with irrelevant features.However, when all features are relevant, the benefits of feature selection may be minimal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it