Machine Learning

How To Implement Logistic Regression From Scratch in Python

Last Updated on December 11, 2019

Logistic regression is the go-to linear classification algorithm for two-class problems.

It is easy to implement, easy to understand and gets great results on a wide variety of problems, even when the expectations the method has of your data are violated.

In this tutorial, you will discover how to implement logistic regression with stochastic gradient descent from scratch with Python.

After completing this tutorial, you will know:

  • How to make predictions with a logistic regression model.
  • How to estimate coefficients using stochastic gradient descent.
  • How to apply logistic regression to a real prediction problem.

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

Let’s get started.

  • Update Jan/2017: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.
  • Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.
  • Update Aug/2018: Tested and updated to work with Python 3.6.
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