Generalized Variable Importance Metric: An approach to identify important predictors from machine learning models
Bibliographic record
Abstract
Interpreting black box machine learning methods posses a significant challenge, with existing approaches often being data and model specific. In this thesis, a “Generalized Variable Importance Metric (GVIM)” is defined to measure predictor importance utilizing black box methods without relying on model-based parameters. GVIM, which is defined for a predictor using the true conditional expectation function, assesses the predictor’s impact on a continuous or binary response. A permutation-based approach to estimate GVIM is proposed in this thesis, akin to those by Breiman (2001) and Fisher et al. (2019a). However, black-box models underestimate GVIM when predictors are correlated. Through a bias-variance decomposition, the source of the bias is identified and its pattern in high correlation scenarios is demonstrated, suggesting ways to minimize it. The primary bias stems from black-box models’ limited ability to extrapolate to regions that have low probability because of the correlations. A conditional GVIM method (CGVIM) based on Strobl et al. (2008) is introduced, its bias-variance decomposition is derived, and its relationship with predictor correlations is shown. Both GVIM and CGVIM exhibited a quadratic relationship with the conditional average treatment effect (CATE). Finally, I demonstrated the application of GVIM and CGVIM to investigate risk factors for cognitive decline using data from the Canadian Longitudinal Study on Aging (CLSA) dataset. The proposed method is model-agnostic and offers a causal interpretation, which is crucial for clinical and public health research. Understanding exposure-outcome relationships is vital in health science, where traditional models like regression are preferred for interpretability, but machine learning excels in prediction. GVIM and CGVIM, being model-agnostic, allow researchers to choose their preferred machine learning model without sacrificing prediction or inference capabilities.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".