: Methods for prediction, including linear regression, classification trees, Neural Networks , Support Vector Machines (SVM) , and Boosting .
: Vital chapters on cross-validation, model selection, and managing the bias-variance tradeoff.
The book covers a broad spectrum of techniques, moving from fundamental supervised learning to complex unsupervised methods: The Elements of Statistical Learning
: Co-inventor of CART (Classification and Regression Trees) , MARS, and Gradient Boosting . Purchase Options
The authors are pioneers in the field who developed many of the tools described in the book: Purchase Options The authors are pioneers in the
: Techniques for finding structure in unlabeled data, such as Clustering , Principal Component Analysis (PCA) , and Non-negative Matrix Factorization.
: Developed Generalized Additive Models ; Tibshirani is the creator of the Lasso . such as Clustering
: It is considered an advanced PhD-level text designed for statisticians, researchers, and anyone interested in the mathematical foundations of data mining and machine learning.