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Show Notes
The Elements of Statistical Learning (Trevor Hastie)
- Amazon USA Store: https://www.amazon.com/dp/0387848576?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/The-Elements-of-Statistical-Learning-Trevor-Hastie.html
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- Read more: https://english.9natree.com/read/0387848576/
#Supervisedlearning #Biasvariancetradeoff #Crossvalidation #Lassoregularization #Randomforests #TheElementsofStatisticalLearning
The Elements of Statistical Learning: Data Mining, Inference, and Prediction is an advanced textbook by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, originally developed for readers who need to understand the principles behind modern predictive and exploratory methods. Its second edition surveys statistical learning as a shared intellectual territory spanning statistics, machine learning, data mining, and bioinformatics. Rather than presenting algorithms as isolated recipes, the book asks what assumptions they make, how they fit data, why they generalize or fail to generalize, and how competing methods should be assessed. It covers linear and nonlinear regression, classification, resampling, regularization, trees, support vector machines, neural networks, ensembles, and unsupervised learning. The exposition is conceptually oriented but technically demanding, relying on probability, linear algebra, optimization, and statistical reasoning. The result is not a beginner programming manual. It is a durable reference for understanding the structure of learning problems and the tradeoffs that govern reliable prediction from data.
- Amazon USA Store: https://www.amazon.com/dp/0387848576?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/The-Elements-of-Statistical-Learning-Trevor-Hastie.html
- Apple Books: https://books.apple.com/us/audiobook/scale-the-universal-laws-of-growth/id1417927080?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=The+Elements+of+Statistical+Learning+Trevor+Hastie+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0387848576/
#Supervisedlearning #Biasvariancetradeoff #Crossvalidation #Lassoregularization #Randomforests #TheElementsofStatisticalLearning
The Elements of Statistical Learning: Data Mining, Inference, and Prediction is an advanced textbook by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, originally developed for readers who need to understand the principles behind modern predictive and exploratory methods. Its second edition surveys statistical learning as a shared intellectual territory spanning statistics, machine learning, data mining, and bioinformatics. Rather than presenting algorithms as isolated recipes, the book asks what assumptions they make, how they fit data, why they generalize or fail to generalize, and how competing methods should be assessed. It covers linear and nonlinear regression, classification, resampling, regularization, trees, support vector machines, neural networks, ensembles, and unsupervised learning. The exposition is conceptually oriented but technically demanding, relying on probability, linear algebra, optimization, and statistical reasoning. The result is not a beginner programming manual. It is a durable reference for understanding the structure of learning problems and the tradeoffs that govern reliable prediction from data.
