MLP4ML: Machine Learning Service Recommendation System using MLP
Bibliographic record
Abstract
In this work, we propose a unique approach for Machine Learning (ML) service recommendation using multilayer perceptron architecture. A service is recommended based on its predicted performance on the input dataset. We take Quality of Services (QoS) as the performance indicator. Depending on the application domain and user requirements, the importance level of different QoS attributes could be different. For ML services, their QoS values are affected by both the input dataset and the service. It would be helpful if we can include their features into the recommendation model. In this work, we consider two types of side information: features of the services and of the user (in our case the dataset given by the user). In the experiment, we take OpenML as our data source and extract QoS values of multiple classification services running on 390 datasets. The result shows that dataset-service interactions can be used to predict the performance of a service on a given dataset. When we integrate all the side information, the performance is better than using the interaction data alone in terms of both prediction and recommendation accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".