Comparison of three data mining algorithms for potential 4G customers prediction
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 size and number of telecom databases are growing quickly but most of the data has not been analyzed for revealing thehidden and valuable intellectual. Models developed from data mining techniques are useful for telecom to make right prediction.The dataset contains one million customers from a telecom company. We implement data mining techniques, i.e. , AdaboostM1(ABM) algorithm, Naïve Bayes (NB) algorithm, Local Outlier Factor (LOF) algorithm to develop the predictive models. Thispaper studies the application of data mining techniques to develop 4G customer predictive models and compares three models onour dataset through precision, recall, and cumulative recall curve. The result is that precision of ABM, NB and LOF are 0.6016,0.6735 and 0.3844. From the aspects of cumulative recall curve NB algorithm also is the best one.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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