Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions
Why this work is in the frame
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Bibliographic record
Abstract
Shapley values are widely used in machine learning to interpret model predictions.However, they have an important drawback in their computational time, which is exponential in the number of variables in the data.Recent work has yielded algorithms that can efficiently and exactly calculate the Shapley values of specific model families, such as Decision Trees and Generalized Additive Models (GAMs).Unfortunately, these model families are fairly restricted.Consequently, we present STAR-SHAP, an algorithm for efficiently calculating the Shapley values of Structured Additive Regression (STAR) models, a generalization of GAMs which allow any number of variable interactions.While the computational cost of STAR-SHAP scales exponentially in the size of these interactions, it is independent of the total number of variables.This allows the interpretation of more complex and flexible models.As long as the variable interactions are moderately-sized, the computation of the Shapley values will be fast, even on high-dimensional datasets.Since STAR models with more than pairwise interactions (e.g.GA2Ms) are seldom used in practice, we also present a new class of STAR models built on the RKHS Weightings of Functions paradigm.More precisely, we introduce a new RKHS Weighting instantiation, and show how to transform it and other RKHS Weightings into STAR models.We therefore introduce a new family of STAR models, as well as the means to interpret their outputs in a timely manner.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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