Tree-Based Methods for Statistical Learning in R (Hardcover)

Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there's an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Data Science Series
Release date:	June 2022
Authors:	Brandon M. Greenwell
Dimensions:	234 x 156 x 31mm (L x W x T)
Format:	Hardcover
Pages:	388
ISBN-13:	978-0-367-53246-8
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Professional & Technical > Environmental engineering & technology > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	0-367-53246-8
Barcode:	9780367532468

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