Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)

Tree models

Decision trees build tree structures to generate regression or classification models. You can think of it as a collection of chained if, and else if statements that will culminate in predictions. These models are very flexible:

  • Categorical and numerical input/output is welcomed
  • Classifications and regressions can be made using tree-based models
  • Trees can grow very long (and complicated) or small (and simple)
Although it's possible to design very complicated trees, it is not recommended to do so. Over-complicated trees tend to be a great source of overfitting.

Needless to say, it is very easy to implement tree-based models with R. Even more complex algorithms that rely on trees as basic building blocks can be easily implemented with R (more on that in the Random forests – a collection of trees,). The current section will make a quick tour through tree...