Corporate marketing based on improved depth-weighted k-mean arithmetic and improved extreme gradient boosting tree
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study firstly tries to segment the value of telecommunication customers through data mining methods, and introduces variable convolution on the basis of depth-weighted K-mean algorithm for improvement. Meanwhile, grid search is introduced on the basis of extreme gradient boosting tree for optimization, and finally a telecom customer recommendation marketing model is proposed by combining the two optimization algorithms. The experiments use a publicly available dataset from Kaggle that contains telecom customer behavior data, call records, billing records, and service usage from China in 2019, totaling about 2 million pieces of information. The experimental results show that the highest value of classification accuracy of the improved depth-weighted K-mean algorithm is 95.5%, and the highest separation degree is 96.3%. In summary, the proposed model can effectively categorize telecom customers and rationally implement telecom product recommendation. The study aims to provide telecom companies with more accurate marketing decision support to improve customer satisfaction and market competitiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it