Detecting influential observations by cluster analysis and Monte Carlo cross-validation
Bibliographic record
Abstract
The detection of influential observations is an essential step for building high performance models and has been recognized as an important and challenging task in many industrial and laboratorial applications. A new approach for detecting influential observations is developed based on their effect on partial least squares (PLS) modeling. In this method, we build a large number of PLS models by using Monte Carlo cross-validation (MCCV), and then perform principal component analysis (PCA) on the regression coefficients of these models. Because a model with influential observations is different from the one without influential observation, the series of PLS models cluster into different groups in principal component (PC) spaces, based on the different number of influential observations they contain. The influential observations can be therefore recognized according to the frequency number of each sample in each group. By three examples quantitatively modeling near-infrared (NIR) and Raman spectra, it was shown that the method can detect the influential observations intuitively and veraciously.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".