Inferential Procedures for Comparing the Accuracy and Intrinsic Measures of Multivariate Receiver Operating Characteristic (MROC) Curve
Bibliographic record
Abstract
A number of classification techniques are prevailing in literature. Of them, one of the most important techniques is the Receiver Operating Characteristic (ROC) curve. A multivariate extension of this technique is proposed in the recent years. This technique helps in classifying the objects/individuals into one of the two classes by considering two or more markers. The most important measure of an ROC curve is the Area Under the Curve (AUC) and it explains the accuracy and discriminating ability of the test under study. There are two intrinsic measures of ROC namely sensitivity (Sn) and specificity (Sp). Further, two ROC curves can be compared by comparing their measures. The practical application of the proposed inferential procedures is explained with the help of two real datasets namely, Indian Liver Patient (ILP) Dataset and Intra Uterine Growth Restricted Fetal Doppler Study (IUGRFDS) dataset. These inferential procedures are developed based on the measures of multivariate ROC (MROC) curve proposed by Sameera G, R Vishnu Vardhan and KVS Sarma [1].
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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 | 0.054 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".