Integrating Multi-Criteria Decision Making and Random Forest Framework for Product Advisory System
Bibliographic record
Abstract
Complexity in decision making of product evaluation, healthcare, finance, and policy areas often refers to trade-offs between a variety of contradictory criteria. The training approaches that have historically always been used in one form or another can rarely capture such complexity and so outcomes that are not accurate representations of what should be given importance in the real world can occur. This study will be focused on creating a general-purpose decision support system that is based on an extensive range of multi-criteria solutions that can be used to make decisions and machine learning to become more reliable and flexible. The model is a hybrid of the Weighted Sum Model, Technique of Order Preference by similarity to ideal solution, Preference Ranking Organization Method of enrichment Evaluation, Analytic hierarchy Process, elimination and choice Expressing Reality, Multi objective optimization by ratio Analysis, complex proportional Assessment, Analytic Network Process and the grey Relational Analysis. Meanwhile, a Random Forest learning model is introduced to merge latent connections with the data and associate the rankings come by the decision-making approaches. Practical testing of the architecture based on product sales records proves that the proposed system can combine user defined preferences with predictive ratings to generate stable and understandable recommendations. The findings suggest that this hybrid model is not restrictive to product choice, but it could be extended in terms of application in some other aspects like service evaluation, patient treatment planning, investment ranking, and policy evaluation. The study introduces a generalizable and customizable approach that takes the decision support beyond the classical models and analytics based on data.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".