Online comment‐based prediction of cosmetic ingredient's sensory irritation using gradient boosting algorithm
Bibliographic record
Abstract
BACKGROUND: The worldwide prevalence of "sensitive skin" group is estimated at being close to 40%. The main trigger for sensitive skin is the misuse of cosmetics products. Majority of the in vitro studies on cosmetic ingredients developed for topical application to the skin are not able to describe sensory irritation, such as stinging, burning, itching, and tingling. Besides, most of the in vivo tests often encounter problems such as limited number of subjects and usage scenarios deviate from reality. OBJECTIVE: A gradient boosting algorithm is adopted in our context to integrate multisource of information including skin types, sensory response, and cosmetics ingredients to predict sensory irritation. METHOD: In this study, online comments were preprocessed to construct a multi-dimensional structured data including skin types, sensory response, and cosmetics ingredients. A gradient boosting regression model was developed where sensory response was predicted based on the abovementioned structured input. The predictions were validated by in vivo test and were shown favorably when comparing with the state-of-the-art results from related works. RESULT: ). In addition, this model was validated using data from in vivo studies and presented a value of 75% on specificity, 88.9% on sensitivity, and 82.4% on accuracy. CONCLUSION: Our approach that is based on a variant of the gradient boosting algorithm provided an adequate solution for understanding the sensory irritation of cosmetic ingredients.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".