Adaptive Method for Machine Learning Model Selection in Data Science Projects
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
Data science projects involve a machine learning (ML) process based on data, code, and models that change over time. For example, the datasets may increase in size and allow an ML model that requires larger datasets to be applied. However, the dynamic factors that influence model selection are not well understood and explicitly represented. This paper presents ongoing work on an adaptive method for ML model selection in big data science projects. The proposed method involves (i) identifying the factors that affect model selection based on heuristics proposed in the literature; and (ii) modeling the variability of these factors using a feature diagram and constraints that trigger adaptive reconfiguration, that is, changes in model selection due to changes in the variability factors. The applicability of the method is demonstrated through an illustrative use case. The proposed method can lead to an improved understanding of dynamic factors that influence model selection, how these factors explicitly affect the selection, and how the adaptive factors can be represented and automated. This improved understanding can result in a project model selection process that is less implicit and more efficient, more adaptive and explainable, and ultimately constitute a foundation for the creation of novel dynamic software product lines to support this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.022 | 0.016 |
| Research integrity | 0.000 | 0.001 |
| 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