Research on telecommunication subscriber churn prediction model based on quadratic classification method
Bibliographic record
Abstract
The development of communication technology and the rapid growth of the number of mobile network service users have made the competitive situation in the market of communication service increasingly fierce, and maintaining the stock of users is of great significance to the sustainable development of telecommunication enterprises.In this paper, we collect relevant data features of telecommunication users, and after pre-processing the features with RFM model, we use XGBoost model to analyze the importance of each user's feature value.Then we use the secondary classification Stacking integration model that combines the base learner and the meta-learner to predict the telecom subscriber churn.Comparative validation reveals that the prediction model in this paper shows excellent prediction performance in all four datasets.Practical application results show that the effectiveness of churn maintenance efforts by telecom companies is improved after applying the model, and the average maintenance response rate reaches 50.63% in the first quarter of 2024.The prediction model proposed in this paper based on the binary classification method can assist telecommunication companies to manage the stock of subscribers, optimize the maintenance work plan, and reduce the subscriber churn rate in the telecommunication work period.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".