Tree-Based Methods for Statistical Learning in R - Chapman & Hall/CRC Data Science Series

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.

ISBN:	9780367532468
Publisher:	Taylor & Francis
Imprint:	Chapman & Hall/CRC
Published date:	23 Jun 2022
DEWEY:	658.403
DEWEY edition:	23
Language:	English
Number of pages:	400
Weight:	748g
Height:	241mm
Width:	160mm
Spine width:	31mm

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