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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
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“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
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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
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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.
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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.
