How can Automated Machine Learning Help Business Data Science Teams?
Bibliographic record
Abstract
Artificial intelligence and machine learning have attracted the attention of many commercial and non-profit organizations aiming to leverage advanced analytics, in order to provide a better service to their customers, increase their revenues through creating new or improving their existing internal processes, and better exploit their data by discovering complex hidden patterns. Such advanced solutions require data scientists with rare (and generally expensive) skill sets. Moreover, such solutions are often perceived as complex black boxes to decision-makers. Automated machine learning tools aim to reduce the expertise gap between the technical teams and stakeholders involved in business data science projects, by reducing the amount of time and specialized skills required to generate predictive models. We systematically benchmarked five automated machine learning tools against seven supervised learning problems of a business nature. Our results suggest that such tools, in fully automated mode, must be used cautiously, only where predictive models support low-impact decisions and do not need to be explainable, and only by data scientists capable to ensure that all phases of the data mining process have been performed adequately.
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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 | 0.034 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".