: Modern topics like the Lasso , Random Forests, and methods for "wide data" where the number of predictors exceeds the number of observations. Authors' Significance
: Co-inventor of CART (Classification and Regression Trees) , MARS, and Gradient Boosting . Purchase Options
: The primary goal is to build prediction models or "learners" that can accurately predict outcomes based on features observed in a training dataset. Key Topics and Content The Elements of Statistical Learning
: While the book is mathematically rigorous, it emphasizes concepts and intuition over pure mathematical proofs, using liberal color graphics and real-world examples from finance, biology, and medicine.
: Methods for prediction, including linear regression, classification trees, Neural Networks , Support Vector Machines (SVM) , and Boosting . : Modern topics like the Lasso , Random
: Vital chapters on cross-validation, model selection, and managing the bias-variance tradeoff.
: Techniques for finding structure in unlabeled data, such as Clustering , Principal Component Analysis (PCA) , and Non-negative Matrix Factorization. Key Topics and Content : While the book
The book covers a broad spectrum of techniques, moving from fundamental supervised learning to complex unsupervised methods: