Book Image

Data Science Algorithms in a Week. - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week. - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science

Chapter 3. Decision Trees

A decision tree is the arrangement of data in a tree structure where, at each node, data is separated into different branches according to the value of the attribute at the node.

To construct a decision tree, we will use a standard ID3 learning algorithm that chooses an attribute that classifies data samples in the best possible way to maximize the information gain—a measure based on information entropy.

In this chapter, we will cover the following topics:

  • What a decision tree is and how to represent data in a decision tree through the swim preference example
  • The concepts of information entropy and information gain, theoretically in the first instance, before applying the swim preference example in practical terms in the Information theory section
  • How to use a ID3 algorithm to construct a decision tree from the training data, and its implementation in Python
  • How to classify new data items using the constructed decision tree through the swim preference example
  • How to carry...