Prediction trees with soft nodes for binary outcomes
Why this work is in the frame
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Bibliographic record
Abstract
Consider the problem of predicting the occurrence of an event, the onset of diabetes mellitus, say, from a vector of continuous and discrete predictors. We propose a new algorithm for the construction of a tree-structured predictor for the event of interest, which uses a new approach for dealing with continuous predictors. The novelty is that the tree uses splits for continuous variables. This means that at each node an individual goes to the right branch with a certain probability, function of a predictor. The predictor as well as the particular shape of the function is chosen from the data by the proposed algorithm. We evaluate its performance on several real data sets, in particular comparing it with a standard tree-growing algorithm. We also present an analysis of a well-known data set, the Pima Indian diabetes data set, to illustrate the application of the method in biostatistics.
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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 | 0.000 | 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