Building diverse and optimized ensembles of gradient boosted trees for high-dimensional data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gradient Boosting Machines (GBMs) are powerful ensemble learning techniques that have been successfully applied to several low-dimensional applications. In GBMs, the learning algorithm sequentially fits new models to provide more accurate prediction of the response variable. Despite their high accuracy, GBMs suffer from major drawbacks such as high memory-consumption. In addition, given the fact that the learning algorithm is essentially sequential, it has problems with parallelization by design. Therefore, building optimized GBMs for high-dimensional applications requires powerful computations resources. In this paper, using real, high-dimensional (i.e. 1776 predictors) dataset, we demonstrate that by using different features selection/reduction techniques, the computations costs for building and tuning Tree-based GBMs can be substantially reduced at a slight drop in prediction accuracy. To cope with the data-intensive computations involved in building and tuning the ensembles, we utilize Amazon Elastic Compute Cloud (EC2) web service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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