Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distribution. Splitting criteria that are relatively insensitive to costs (class distributions) are found to perform as well as or better than, in terms of expected misclassification cost, splitting criteria that are cost sensitive. Consequently there are two opposite ways of dealing with imbalance. One is to combine a costinsensitive splitting criterion with a cost insensitive pruning method to produce a decision tree algorithm little affected by cost or prior class distribution. The other is to grow a cost-independent tree which is then pruned in a cost-sensitive manner. 1. Introduction When applying machine learning to real world classification problems two complications that often arise are imbalanced classes (one class occurs much more often than the other (Kubat et al., 1998; Ezawa et al., 1...
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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