The DOI matches its Crossref record.
The Elements of Statistical Learning
Abstract
Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To overcome the black-box nature of GBM, (Breiman 2001; Friedman and Popescu 2008; Hastie et al. 2009) introduced two important tools to interpret the GBM model: (i) variable importance measures and (ii) partial dependence plots.”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Hastie et al. 2009
In the citing paper
“The training data is divided into a learning set (80%) and a validation set (20%) considering random folds of nearly the same size, mutually exclusive, and stratified (Hastie et al. 2009).”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Hastie et al. 2009
In the citing paper
“See, for example,Jaggi (2013) andHastie, Tibshirani, and Friedman (2009), who discuss the equivalence of support vector machines with the lasso.”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Hastie, Tibshirani, and Friedman (2009)
In the citing paper
“Among these three types of impurity function, cross-entropy and the Gini index are more suitable than the misclassification rate when growing a tree.36”
From Predicting bitcoin returns using high-dimensional technical indicators (2019) · cites this paper as [36]
In the citing paper
“That is, for a given distribution, the optimal bandwidth depends on the number of examples (key-value pairs in associative memory).”
From Memory Mosaics at scale (2025) · cites this paper as Hastie et al., 2009
9 more claims about this paper
Create a free account to see every claim other papers make about this one.
Citations
Cited by
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,584
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Self-citation
Metadata
- Authors
- Hastie, T., Tibshirani, R., & Friedman, J.
- Affiliations
- Stanford University (US)
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 2009
- ISSN
- 0172-7397 (ISSN-L)
- 2197-568X
- OpenAlex
- W2787894218
- Cited by
- 25,970
- References
- 0
- Citations by year
- 2026: 3306 · 2025: 3252 · 2024: 2901 · 2023: 2360 · 2022: 2154 · 2021: 2134 · 2020: 1664 · 2019: 1391 · 2018: 1170 · 2017: 1082 · 2016: 1024 · 2015: 1009 · 2014: 725 · 2013: 623 · 2012: 513
- FWCI
- 61.16
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer series in statistics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- statistical learning, machine learning, statistical inference, data analysis, applied statistics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Elements of Statistical Learning. https://app.cochat.ai/papers/10.1007/978-0-387-84858-7
- The paper
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer Series in Statistics. https://doi.org/10.1007/978-0-387-84858-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
