An introduction to statistical learning : with applications in R

An introduction to statistical learning : with applications in R

Enregistré dans:
Détails bibliographiques
Auteurs principaux : James Gareth (Auteur), Witten Daniela (Auteur), Hastie Trevor J. (Auteur), Tibshirani Robert John (Auteur)
Format : Livre
Langue : anglais
Titre complet : An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie... [et al.]
Publié : New York : Springer , C 2013
Description matérielle : 1 vol. (XIV-426 p.)
Collection : Springer texts in statistics advisors George Casella, Stephen Fienberg, Ingram Olkin
Sujets :
Statistique mathématique > Problèmes et exercices
Modèles mathématiques > Problèmes et exercices
R
Statistique
Analyse multivariée
Statistiques
Modèles statistiques
Interprétation statistique de données
Documents associés : Autre format: An introduction to statistical learning
  • 1 Introduction
  • 2 Statistical Learning
  • 2.1 What Is Statistical Learning?
  • 2.2 Assessing Model Accuracy
  • 2.3 Lab: Introduction to R
  • 2.4 Exercises
  • 3 Linear Regression
  • 3.1 Simple Linear Regression
  • 3.2 Multiple Linear Regression
  • 3.3 Other Considerations in the Regression Mode
  • 3.4 The Marketing Plan
  • 3.5 Comparison of Linear Regression with K-Nearest Neighbors
  • 3.6 Lab: Linear Regression
  • 3.7 Exercises
  • 4 Classification
  • 4.1 An Overview of Classification
  • 4.2 Why Not Linear Regression?
  • 4.3 Logistic Regression
  • 4.5 A Comparison of Classification Methods
  • 4.6 Lab: Logistic Regression, LDA, QDA, and KNN
  • 4.7 Exercises
  • 5 Resampling Methods
  • 5.1 Cross-Validation
  • 5.2 The Bootstrap
  • 5.3 Lab: Cross-Validation and the Bootstrap
  • 5.4 Exercises
  • 6 Linear Model Selection and Regularization
  • 6.1 Subset Selection
  • 6.2 Shrinkage Methods
  • 6.3 Dimension Reduction Methods
  • 6.4 Considerations in High Dimensions
  • 6.5 Lab 1: Subset Selection Methods
  • 6.6 Lab 2: Ridge Regression and the Lasso
  • 6.7 Lab 3: PCR and PLS Regression
  • 6.8 Exercises
  • 7 Moving Beyond Linearity
  • 7.1 Polynomial Regression
  • 7.2 Step Functions
  • 7.3 Basis Functions
  • 7.4 Regression Splines
  • 7.6 Local Regression
  • 7.7 Generalized Additive Models
  • 7.8 Lab: Non-linear Modeling
  • 7.9 Exercises
  • 8 Tree-Based Methods
  • 8.1 The Basics of Decision Trees
  • 8.2 Bagging, Random Forests, Boosting
  • 8.3 Lab: Decision Trees
  • 8.4 Exercises
  • 9 Support Vector Machines
  • 9.1 Maximal Margin Classifier
  • 9.2 Support Vector Classifiers
  • 9.3 Support Vector Machines
  • 9.4 SVMs with More than Two Classes
  • 9.5 Relationship to Logistic Regression
  • 9.6 Lab: Support Vector
  • 9.7 Exercises
  • 10 Unsupervised Learning
  • 10.1 The Challenge of Unsupervised Learning
  • 10.2 Principal Components Analysis
  • 10.3 Clustering Methods
  • 10.4 Lab 1: Principal Components Analysis
  • 10.5 Lab 2: Clustering
  • 10.6 Lab 3: NCI60 Data Example
  • 10.7 Exercises