Gram-Schmidt Orthogonalization for Feature Ranking and Selection — A Case Study of Claim Prediction
Bibliographic record
Abstract
Claim prediction is an important process in the insurance industry to prepare the right type of insurance policy for each potential policyholder. The frequency of claim predictions is highly increasing that head the problem of big data in terms of both the number of features and the number of policyholders. One of machine learning paradigms to handle the problem of the big data is dimensionality reduction by using a feature selection method. In this paper, we examine a new feature selection method for claim prediction using Gram-Schmidt Orthogonalization. In this method, the next features are iteratively selected based on the farthest distance to space spanned by the current features. Therefore, the advantage of the Gram-Schmidt Orthogonalization method is that it can provide a subset of the feature ranking without ordering all features. Our simulation shows that by using only about 26% of features, the predictor can reach comparable accuracy when it uses all features. It means that the Gram-Schmidt Orthogonalization-based feature selection method may need memory usage of about 26%, which is very significant in the context of the Big Data problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".