Construction of Corporate Financial Risk Prediction Model Based on Random Forest Algorithm
Bibliographic record
Abstract
Financial risk has a greater impact on the operation and development of enterprises, and accurate prediction of financial risk has become an industry demand, so as to better help enterprises avoid possible financial risk.The article establishes an enterprise financial risk prediction model based on the random forest algorithm, and fills in the oversampling of financial data through the SMOTENC algorithm, and realizes the downsizing of financial data by combining with the KPCA algorithm.Based on the enterprise financial risk characterization index system, the financial data of 358 listed enterprises were selected to carry out model validation and application analysis.The accuracy of corporate financial risk prediction based on Random Forest can reach up to 94.17%, and the average value of the overall time efficiency of the model is 0.68%, which is faster than the comparison algorithm in terms of financial data processing capability.Based on the results of financial risk prediction, the changes in corporate profitability, operating ability, solvency and development ability can be analyzed in depth, providing data support for enterprises to formulate preventive measures for corporate financial risk.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".