Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

Decision Trees


Like logistic regression, there is another popular classification technique that is very popular due to its simplicity and white-box nature. A decision tree is a simple flowchart that is represented in the form of a tree (an inverted tree). It starts with a root node and branches into several nodes, which can be traversed based on a decision, and ends with a leaf node where the final outcome is determined. Decision trees can be used for regression, as well as classification use cases. There are several variations of decision trees implemented in machine learning. A few popular choices are listed here:

  • Iterative Dichotomiser 3 (ID3)

  • Successor to ID3 (C4.5)

  • Classification and Regression Tree (CART)

  • CHi-squared Automatic Interaction Detector (CHAID)

  • Conditional Inference Trees (C Trees)

The preceding list is not exhaustive. There are other alternatives, and each of them has small variations in how they approach the tree creation process. In this chapter, we will limit our exploration...