The Elements of Statistical Learning is a comprehensive and authoritative text on statistical learning, data mining, and predictive modeling. Written by renowned experts Trevor Hastie, Robert Tibshirani, and Jerome Friedman, the book provides a detailed exploration of the theoretical foundations and practical applications of various statistical learning techniques. The text covers a broad range of topics, including linear regression, classification, decision trees, support vector machines, neural networks, and ensemble methods, among others. It emphasizes the interplay between data analysis, statistical theory, and computational algorithms, making it an essential resource for both practitioners and researchers in the field of data science and machine learning. Throughout the book, the authors focus on the principles of model selection, overfitting, and the bias-variance tradeoff, explaining how to choose the right model for a given dataset and problem. The book also highlights key concepts in model evaluation, including cross-validation and resampling techniques, and introduces advanced topics such as unsupervised learning, clustering, and nonparametric methods.
The Elements of Statistical Learning Data Mining, Inference, and Prediction
- Auteur: Trevor Hastie, Robert Tibshirani, Jerome Friedman
- ISBN:
- Categorie: Livres
- Maison Edition: Springer
- Ville Edition:
- Année Edition: 2017
- Domaine: Intelligence Artificielle
