by Brandon M. Greenwell (Author)
Tree-based Methods for Statistical Learning in R
provides a thorough introduction to both individual decision tree
algorithms (Part I) and ensembles thereof (Part II). Part I of the book
brings several different tree algorithms into focus, both conventional
and contemporary. Building a strong foundation for how individual
decision trees work will help readers better understand tree-based
ensembles at a deeper level, which lie at the cutting edge of modern
statistical and machine learning methodology.
The
book follows up most ideas and mathematical concepts with code-based
examples in the R statistical language; with an emphasis on using as few
external packages as possible. For example, users will be exposed to
writing their own random forest and gradient tree boosting functions
using simple for loops and basic tree fitting software (like rpart and party/partykit),
and more. The core chapters also end with a detailed section on
relevant software in both R and other opensource alternatives (e.g.,
Python, Spark, and Julia), and example usage on real data sets. While
the book mostly uses R, it is meant to be equally accessible and useful
to non-R programmers.
Consumers of this book will
have gained a solid foundation (and appreciation) for tree-based methods
and how they can be used to solve practical problems and challenges
data scientists often face in applied work.
Features:
- 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.
