Analysis of imbalanced data set problem: The case of churn prediction for telecommunication
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
Class-imbalanced datasets are common in the field of mobile Internet industry. We tested three kinds of feature selection techniques-Random Forest (RF), Relative Weight (RW) and Standardized Regression Coefficients (SRC); three kinds of balance methods-over-sampling (OS), under-sampling (US) and synthetic minority over-sampling (SMOTE); a widely used classification method-RF. The combined models are composed of feature selection techniques, balancing techniques and classification method. The original dataset which has 45 thousand records and 22 features were used to evaluate the performances of both feature selection and balancing techniques. The experimental results revealed that SRC combined with SMOTE technique attained the minimum value of Cost = 1085. Through the calculation of the Cost on all models, the most important features for minimum cost of telecommunication were identified. The application of these combined models will have the possibility to maximize the profit with the minimum expenditure for customer retention and help reduce customer churn rates.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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