Visualization Techniques for Confidence Based Data
Bibliographic record
Abstract
Decision support algorithms form an important part of the larger world of data mining. The purpose of a decision support system is to provide a human user with the context surrounding a complex decision to be based on computational analysis of the data at hand. Typically, the data to be considered cannot be adequately managed by a human decision maker because of its volume, complexity or both; data mining techniques are therefore used to discover patterns in the data and inform the user of their saliency in terms of a particular decision to be made. Visualization plays an important role in decision support, as it is through visualization that we can most easily comprehend complex data relationships (Tufte, 1997, 2001, 2006; Wright, 1997). Visualization provides a means of interfacing computationally discovered patterns with the strong pattern recognition system of the human brain. As designers of visualization for decision support systems, our task is to present computational data in ways that make intuitive sense based on our knowledge of the brain’s aptitudes and visual processing preferences. Confidence, in the context of a decision support system, is an estimate of the value a user should place in the suggestion made by the system. System reliability is the measure of overall accuracy; confidence is an estimate of the accuracy of the suggestion currently being presented. The idea of an associated confidence or certainty value in decision support systems has been incorporated in systems as early as MYCIN (Shortliffe, 1976; Buchanan & Shortliffe, 1984). Request access from your librarian to read this chapter's full text.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".