Statistical Learning from a Regression Perspective by BERK, R. A.
Bibliographic record
Abstract
This book is unique in that statistical learning is discussed by a sociology–PhD scientist, Professor Richard Berk, who has extensive research accomplishments in the intersection of social science and statistics. It covers a subset of statistical-learning methods discussed by, and follows the notations used in, a popular statistical-learning book by Hastie, Tibshirani, and Friedman (2009), “The Elements of Statistical Learning” (hereafter referred to as ESL). Specifically, the following statistical-learning methods are discussed: regression splines and regression smoothers (Chapter 2), classification and regression trees (CART) (Chapter 3), bagging (Chapter 4), random forests (Chapter 5), boosting (Chapter 6), and support vector machines (Chapter 7). This book, however, differs from ESL in a number of important aspects. Its key features are summarized below. The key strength of this book is in its emphasis on practical applications and hands-on learning of the statistical-learning methods. Each chapter has real data examples (mostly from social science applications) and goes through their analyses using statistical software R (2009). This design effectively illustrates the use of the methods in practice. “Software consideration” given at the end of each chapter provides discussions on currently available computational tools, both functions/packages of R and other software, and is useful in practice. Emphasis on using R that is freely available worldwide is a major advantage in terms of readers' accessibility to the methods. Furthermore, each chapter contains exercises for practicing different aspects of the methods in the chapter. The solutions and R codes of these exercises are provided at the author's website http://www-stat.wharton.upenn.edu/~berkr/: this is another useful feature enhancing the hands-on learning.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".