August 29, 2013 Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0 Date 2013-06-10 Author Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani Maintainer Trevor Hastie Suggests MASS Description The collection of datasets used in the book ‘‘An Introduction to Statistical Learning with Applications in R’’ License ...

We are grateful that Hastie points out the connection to degrees of freedom for LARS which leads to another—and often better—definition of degrees of freedom for boosting in generalized linear models. As Hastie writes and as we said in the paper, our formula for degrees of freedom is only an approximation: the cost of searching, for example, for the best variable in componentwise linear least s...

We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the ‘best’ method for finite-sample estimation problems, even for the simple case of linear estimators. This paper presents empirical comparisons between classical statistical methods (AIC, BIC) and the SRM method (based on VC-theory) for regression problems. Our study is motivated by ...

This paper introduces a combining classifier using two proposed precision indexes: precision index (PIN) and class specific precision index (PIC). Comparison of combining methods typically fails to consider high precision performance. This new combining method generates predictions with higher precision and recall than other methods. The proposed method is especially useful for efficient screen...

This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these ndings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1990).

Logistic regression1 has been widely used in direct marketing applications to develop response and conversion models. Although attractively simple, logistic regression has been criticized for failing to capture the nonlinearity, and therefore possibly not leading to satisfactory results. Introduced by Hastie and Tibshirani, Generalized Additive Model2 (GAM) provides the ability to detect the no...

The task of learning useful models from available data is common in virtually all fields of science, engineering, and finance. The goal of the learning task is to estimate unknown (input, output) dependency (or model) from training data (consisting of a finite number of samples) with good prediction (generalization) capabilities for future (test) data (Cherkassky & Mulier, 2007; Hastie et al., ...