Model of Gradient Boosting Random Forest Prediction
Bibliographic record
Abstract
Random forests (RF) is an ensemble classification approach, which is easy to use and is helpful to avoid over-fitting. However, in the complex data environment, its prediction accuracy could be deteriorated. Gradient boosting decision tree (GBDT) is another widely used in classification problems because of its high prediction accuracy and interpretability. In order to improve the performance of random forest in solving classification problems, this paper proposes a gradient boosting random forest (GBRF) algorithm. GBRF algorithm employs the idea of gradient to optimize decision tree at the bottom of random forest into gradient boosting decision tree, which improves the prediction accuracy of the bottom tree, and thus improves the prediction performance of random forest. To verify the effectiveness of GBRF algorithm, data sets in UCI and KEEL are used for group testing. The results show that the classification accuracy of GBRF algorithm has a higher prediction accuracy improvement compared to random forest and the performance improvement is more than 5 percent, which indicates that GBRF algorithm performs better than the original random forest.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".