Research on the Construction of Enterprise Data Asset Pricing Model Based on Machine Learning in Digital Economy Environment
Bibliographic record
Abstract
In order to solve the enterprise data asset pricing problem in the digital economy environment, this paper utilizes machine learning algorithms such as multiple regression model, BP neural network, and random forest regression, respectively, to price enterprise data assets.Subsequently, the data obtained from each model is fused using the integrated Stacking algorithm to construct an enterprise data asset pricing model with integrated machine learning algorithms.Predictive estimation of the pricing of enterprise data assets is carried out after a detailed justification of the parameter selection of the model.The results show that data capacity, size, quality and freshness are the main influences on data asset pricing.The results of the parameter investigation show that the overall performance of the model is best when the number of node features is 7, at which time the explanatory degree and goodness of fit of the model are 94.33% and 97.27%, respectively.The accuracy, precision, recall and F1 value of the Stacking-based fusion model for enterprise data asset pricing prediction model increased by about 10% compared to the other three models, respectively, to achieve accurate pricing of enterprise data assets.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".