The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition

By Trevor J. Hastie, Robert Tibshirani, Jerome H. Friedman. 2009

11,969 citations recorded by the source corpus.

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Deep double descent: where bigger models and more data hurt (2019, background): “Hastie et al. (2005)).”
Learning Optimized Risk Scores (2016, methodology): “uge the 6 Ustun and Rudin in uence of each input variable and understand how multiple input variables are combined to output an estimate of predicted risk (as per the denition of interpretability in Hastie et al., 2011). Risk Score Problem In this paper, we learn the values of the coecients from the”
Recursive partitioning for heterogeneous causal effects (2015, methodology): “[9]) has developed a variety of effective methods for a closely related problem, the problem of predicting outcomes as a function of covariates in similar environments.”
Sparse additive models (2007, methodology): “Here we consider an email spam classification problem, using the logistic SpAM backfitting algorithm from Section 3. This dataset has been studied by Hastie et al. (2001) , using a set of 3,065 emails as a training set, and conducting hypothesis tests to choose significant variables; there are a to”
Consistency of Random Forests (2014, methodology): “Section 5 is devoted to the main proofs, and technical results are postponed to Section 6.”
Distributed optimization of deeply nested systems (2012, methodology): “We use the well-known AIC criterion (Hastie et al., 2009).”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “Probabilistic inference vastly inﬂuenced artiﬁcial intelligence and statistical learning and the inverse probability allows to infer unknowns, learn from data and make predictions [6, 7].”
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, methodology): “Also, L1 regularization promotes sparsity in the model (Hastie et al. 2001), which is in line with our goal of finding particular merchants that are informative.”
Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern (2014, background): “Because the number of parameters in a Maxent model may not accurately estimate the degrees of freedom (Hastie et al., 2009), AICc may excessively penalize overparameterization.”
Hierarchical Species Distribution Models (2016, methodology): “Other commonly used methods that rely on basis functions include regression trees [29, 44], some maximum entropy methods (e.”
Item Ordering and Computerized Classification Tests With Cluster-Based Scoring: An Investigation of the Countdown Method (2017, background): “By separating the training and evaluation data sets from each other, the capitalization on chance problem is avoided (Hastie et al., 2009).”
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, positive): “First, as any prediction error the internal prediction error depends on the size of the data set that was used to estimate it (here: the construction data set size (nconstr) in the inner loop) [2,4].”