Optimal Granularity of Machine Learning Models: A Perspective of Granular Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designing machine learning models followed by their deployment in a real-world environment has been an area of recent pursuits, resulting in a large number of successful applications. In particular, these applications target environments that call for a great deal of autonomy and criticality of the developed constructs and ensuing decision processes. An efficient design, carefully structured advanced architecture, high performance, and efficient learning methods are of paramount importance. Equally desired is the confidence of any result produced by the numeric model. In this study, we advocate that the associated information granularity of the numeric models and their results inherently link with the notion of specificity of information granularity. The confidence of results can be quantified in the form of an information granule where the two associated criteria of granular outcomes, such as coverage and specificity, are crucial to the holistic evaluation of the granularity of the results. It is shown that these two characteristics are conflicting and their quality becomes evaluated and optimized. Two main approaches are studied in depth. The first one concerns a granular embedding of numeric models. In the second one, we consider a synergistic environment of Gaussian process models whose results come as probabilistic information granules and can be transformed into interval information granules. An interesting architecture of a rule-based model constructed with the use of innovative clustering takes into account the generative-discriminative aspect of the process of structure discovery, which is accomplished through the optimization of some augmented objective functions. This model is investigated with regard to the two approaches to the design of the mechanism of granular assessment of results. Some illustrative examples are covered to show the essentials of the design process.
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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 | 0.001 |
| 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