Tree-Based Methods for Statistical Learning in R av Brandon M. (University of Cincinnati Cincinnati USA) Greenwell

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<p><b><i>Tree-based Methods for Statistical Learning in R</i></b> 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.</p><p>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 <b>rpart</b> and <b>party</b>/<b>partykit</b>), and more. The core chapters also end with a detailed section on relevant softw