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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Tree-Based Methods for Statistical Learning in R (Hardcover)
Loot Price: R2,816
Discovery Miles 28 160
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Tree-Based Methods for Statistical Learning in R (Hardcover)
Series: Chapman & Hall/CRC Data Science Series
Expected to ship within 10 - 15 working days
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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.
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