Statistical Learning with Sparsity. The Lasso and Generalizations, Paperback/***

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generali Publisher: Taylor & Francis Ltd Author(s): Martin (Department of Statistics, University of California, Berkeley) Wainwright Number of pages: 367 Collection: Chapman & Hall/CRC Monographs on Statistics and Applied Probability Publication date: 2020 Dimensions: 156 x 234 x 23 Cover type: Paperback / softback