Prediction with missing data via Bayesian Additive Regression Trees
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
Abstract We present a method for incorporating missing data into general prediction problems which use nonparametric statistical learning. We focus on a tree‐based method, Bayesian Additive Regression Trees ( BART ), enhanced with “Missingness Incorporated in Attributes,” a recently proposed approach for incorporating missingness into decision trees. This procedure extends the native partitioning mechanisms found in tree‐based models and does not require imputation. Simulations on generated models and real data indicate that our procedure offers promise for both selection model and pattern‐mixture frameworks as measured by out‐of‐sample predictive accuracy. We also illustrate BART 's abilities to incorporate missingness into uncertainty intervals. Our implementation is readily available in the R package bartMachine . The Canadian Journal of Statistics 43: 224–239; 2015 © 2015 Statistical Society of Canada
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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