Rule Extraction from Decision Trees Ensembles: New Algorithms Based on Heuristic Search and Sparse Group Lasso Methods
Bibliographic record
Abstract
Decision trees are examples of easily interpretable models whose predictive accuracy is normally low. In comparison, decision tree ensembles (DTEs) such as random forest (RF) exhibit high predictive accuracy while being regarded as black-box models. We propose three new rule extraction algorithms from DTEs. The RF[Formula: see text]DHC method, a hill climbing method with downhill moves (DHC), is used to search for a rule set that decreases the number of rules dramatically. In the RF[Formula: see text]SGL and RF[Formula: see text]MSGL methods, the sparse group lasso (SGL) method, and the multiclass SGL (MSGL) method are employed respectively to find a sparse weight vector corresponding to the rules generated by RF. Experimental results with 24 data sets show that the proposed methods outperform similar state-of-the-art methods, in terms of human comprehensibility, by greatly reducing the number of rules and limiting the number of antecedents in the retained rules, while preserving the same level of accuracy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".