An Introduction to Statistical Learning

By Gareth M. James, Daniela M. Witten, Trevor J. Hastie, Robert Tibshirani. 2013

10,289 citations recorded by the source corpus.

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “This is the main reason why a training-testing approach is used when dealing with ML problems [162, 163].”
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, background): “One example is found in the k-means clustering algorithm for unsupervised learning [35, 36], where the non-IID nature of the data distribution is seen as a major obstacle in a federated setting.”
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, critical): “[1, 38] However, contemporary ML research and education tends to focus less on obtaining and validating such a training dataset, with such considerations often passed over in major textbooks [e.g. 13, 18, 27].”
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization (2020, background): “A popular textbook (James et al., 2013), for example, claims that “though it is possible to perfectly ﬁt the training data in the high-dimensional setting, the resulting linear model will perform extremely poorly on an independent test set, and therefore does not constitute a useful model.””
Tree species classification using structural features derived from terrestrial laser scanning (2020, methodology): “A second popular classifier is the multinomial logistic regression (MLR) classifier which assigns an observation to the class with the highest probability (James et al., 2013).”
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, methodology): “We chose unsupervised learning because it is commonly used when existing studies have few observations of or insights into unstructured text data [19].”
A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, neutral): “RFs are ensembles of decision trees and work by introducing decorrelation between the trees by randomly selecting a small set of predictors at each split of the tree (James et al. 2013).”
How Valuable Is FinTech Innovation? (2019, neutral): “This cross-validation method has the advantage of being robust and utilizing the entire training sample (see, e.g., chapter 5 of James et al. 2013).”