Extending Scikit-Learn with Your Own Regressor

Learn how to extend Scikit-Learn by creating your own robust linear estimator in this EuroPython 2014 conference talk. Explore the design and inner workings of Scikit-Learn, then follow a practical demonstration of implementing the Theil-Sen estimator, known for its resilience to outliers. Compare the advantages of this estimator to the ordinary least squares method, and gain insights into the requirements and process of contributing to Scikit-Learn. Discover the steps to write a custom regressor that adheres to Scikit-Learn's interfaces, and benefit from the speaker's firsthand experience with submitting a pull request to the project.

Intro
blue yonder
What is Scikit-Learn?
Scikit-Learn's basic areas of application
Least Squares/Linear Regression
Problems with Outliers
How Theil Sen avoids Outliers
Thell-Sen vs. Least Squares
Writing an own Estimator Regressor
Requirements of a Contribution to Scikit-Learn
Experiences of my first Scikit-Learn PR