The Convergence Behavior of Naive Bayes on Large Sparse Datasets
Why this work is in the frame
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Bibliographic record
Abstract
Large and sparse datasets with a lot of missing values are common in the big data era, such as user behaviors over a large number of items. Classification in such datasets is an important topic for machine learning and data mining. Practically, naive Bayes is still a popular classification algorithm for large sparse datasets, as its time and space complexity scales linearly with the size of non-missing values. However, several important questions about the behavior of naive Bayes are yet to be answered. For example, how different mechanisms of data missing, data sparsity, and the number of attributes systematically affect the learning curves and convergence? In this paper, we address several common data missing mechanisms and propose novel data generation methods based on these mechanisms. We generate large and sparse data systematically, and study the entire AUC (Area Under ROC Curve) learning curve and convergence behavior of naive Bayes. We not only have several important experiment observations, but also provide detailed theoretic studies. Finally, we summarize our empirical and theoretic results as an intuitive decision flowchart and a useful guideline for classifying large sparse datasets in practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.007 | 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