Stochastic Stepwise Ensembles for Variable Selection
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Bibliographic record
Abstract
Ensembles methods such as AdaBoost, Bagging and Random Forest have attracted much attention in the statistical learning community in the last 15 years. Zhu and Chipman (2006) proposed the idea of using ensembles for variable selection. Their implementation used a parallel genetic algorithm (PGA). In this thesis, I propose a stochastic stepwise ensemble for variable selection, which improves upon PGA. \nTraditional stepwise regression (Efroymson 1960) combines forward and backward selection. One step of forward selection is followed by one step of backward selection. In the forward step, each variable other than those already included is added to the current model, one at a time, and the one that can best improve the objective function is retained. In the backward step, each variable already included is deleted from the current model, one at a time, and the one that can best improve the objective function is discarded. The algorithm continues until no improvement can be made by either the forward or the backward step. \n Instead of adding or deleting one variable at a time, Stochastic Stepwise Algorithm (STST) adds or deletes a group of variables at a time, where the group size is randomly decided. In traditional stepwise, the group size is one and each candidate variable is assessed. When the group size is larger than one, as is often the case for STST, the total number of variable groups can be quite large. Instead of evaluating all possible groups, only a few randomly selected groups are assessed and the best one is chosen. \nFrom a methodological point of view, the improvement of STST ensemble over PGA is due to the use of a more structured way to construct the ensemble; this allows us to better control over the strength-diversity tradeoff established by Breiman (2001). In fact, there is no mechanism to control this fundamental tradeoff in PGA. Empirically, the improvement is most prominent when a true variable in the model has a relatively small coefficient (relative to other true variables). I show empirically that PGA has a much higher probability of missing that variable.
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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 | 0.002 |
| 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