Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Table of Contents (9 chapters)

Chapter 4. Classification


Learning Objectives

By the end of this chapter, you will be able to:

  • Implement logistic regression and explain how it can be used to classify data into specific groups or classes

  • Use the K-nearest neighbors clustering algorithm for classification

  • Use decision trees for data classification, including the ID3 algorithm

  • Describe the concept of entropy within data

  • Explain how decision trees such as ID3 aim to reduce entropy

  • Use decision trees for data classification


This chapter introduces classification problems, classification using linear and logistic regression, K-nearest neighbors classification, and decision trees.