Explainable Artificial Intelligence for Data Science on Customer Churn
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
Machine learning, as a tool, has become critical for decision-making mechanisms in the modern world. It has applications in a wide range of areas, including finance, healthcare, justice, and transportation. Unfortunately, machine learning is often considered as a “black box”. As such, recommendations made by machine learning techniques, as well as the reasoning behind those recommendations, are not easily understood by humans. In this paper, we present an explainable artificial intelligence (XAI) solution that integrates and enhances state-of-the-art techniques to produce understandable and practical explanations to end-users. To evaluate the effectiveness of our XAI solution for data science, we conduct a case study on applying our solution to explaining a random forest-based predictive model on customer churn. Results show the practicality and usefulness of our XAI solution in practical applications such as data science on customer churn.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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