Variable selection heuristics and optimum decision trees-an experimental study
Bibliographic record
Abstract
Given a decision table it is often the case to find among its trees one tree with the minimum, or at least small, number of the nodes of the tree. It is known that a DP based algorithm works to obtain an optimal tree requiring O(3/sup L/) computation (comparisons). On the other hand a top-down method based on a variable selection method (VSM) choosing a variable at each node according to some simple heuristic can be devised to obtain near optimum trees requiring less computation. We briefly review 3 such heuristics /spl Gamma//sub A/, /spl Gamma//sub H/ and /spl Gamma//sub D/ motivated by the three different standpoints (among them one based on discriminant analysis is new) and their behaviors. Then we present experimental data showing that near optimizations they achieve reflect their respective behaviors. All the heuristics require at most O(L/sup 2/2/sup L/) operations with O(L2/sup L/) storage, where L is the number of variables of the input table.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".