Jerome H. Friedman
· Professor of StatisticsStanford University · Statistics
Active 1955–2024
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About
Jerome H. Friedman is a Professor Emeritus of Statistics at Stanford University with over 20 years of service in the department. He is recognized as one of the world's leading researchers in statistics and data mining, with a primary research interest in machine learning. His extensive work includes publications on a wide range of data mining topics such as nearest neighbor classification, logistical regressions, and high-dimensional data analysis. Dr. Friedman has made significant contributions to the field of data science, and his research continues to influence the development of statistical methods and machine learning techniques.
Research topics
- Computer Science
- Machine Learning
- Data Mining
- Artificial Intelligence
- Mathematics
- Statistics
- Programming language
- Econometrics
- Applied mathematics
Selected publications
The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition
2020 · 232 citations
Senior authorCorrespondinghttps://stars.library.ucf.edu/etextbooks/1452/thumbnail.jpg
Building more accurate decision trees with the additive tree
Proceedings of the National Academy of Sciences · 2019-09-16 · 96 citations
articleOpen accessThe expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision tr…
Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.1-1]
2021 · 82 citations
1st authorCorresponding2017-10-19 · 73 citations
book-chapter2017-10-19 · 54 citations
book-chapter
Recent grants
Topics in Predictive and Descriptive Data Mining
NSF · $420k · 2002–2008
Frequent coauthors
- 70 shared
Stanley M. Flatté
University of California, Santa Cruz
- 64 shared
Phillip Kott
Stanford University
- 64 shared
Jae Lee
- 64 shared
Wen‐Hua Ju
- 64 shared
Patrick Tendick
- 64 shared
Michael Friendly
- 64 shared
Gábor J. Székely
- 64 shared
Carlo di Lauro
University of Naples Federico II
Awards & honors
- Named the applied statistics thesis prize for our emeritus c…
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