4 Distance Measures for Machine Learning

Last Updated on August 19, 2020

Distance measures play an important role in machine learning.

They provide the foundation for many popular and effective machine learning algorithms like k-nearest neighbors for supervised learning and k-means clustering for unsupervised learning.

Different distance measures must be chosen and used depending on the types of the data. As such, it is important to know how to implement and calculate a range of different popular distance measures and the intuitions for the resulting scores.

In this tutorial, you will discover distance measures in machine learning.

After completing this tutorial, you will know:

• The role and importance of distance measures in machine learning algorithms.
• How to implement and calculate Hamming, Euclidean, and Manhattan distance measures.
• How to implement and calculate the Minkowski distance that generalizes the Euclidean and Manhattan distance measures.

Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.