Introducción al modelo clásico de regresión para científicos de datos en R
Bibliographic record
Abstract
En la práctica es común encontrarse con científicos de datos que emplean el modelo de regresión múltiple para resolver preguntas de negocio. Si bien es popular ese uso, es poco frecuente observar en la práctica el chequeo de todos los supuestos que están detrás de este modelo y que hacen que éste pueda generar respuestas adecuadas. El objetivo de este libro es presentar el modelo estadístico clásico de regresión múltiple con toda la formalidad posible a los científicos de datos. Para lograr este objetivo se presenta una mezcla entre los fundamentos (estadísticos y de álgebra lineal) teóricos del modelo y cómo llevarlo a la práctica empleando R.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.034 |
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".