We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Extending Scikit-Learn with your own Regressor

Formal Metadata

Title
Extending Scikit-Learn with your own Regressor
Title of Series
Part Number
64
Number of Parts
119
Author
License
CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language
Production PlaceBerlin

Content Metadata

Subject Area
Genre
Abstract
Florian Wilhelm - Extending Scikit-Learn with your own Regressor We show how to write your own robust linear estimator within the Scikit-Learn framework using as an example the Theil-Sen estimator known as "the most popular nonparametric technique for estimating a linear trend". ----- Scikit-Learn is a well-known and popular framework for machine learning that is used by Data Scientists all over the world. We show in a practical way how you can add your own estimator following the interfaces of Scikit-Learn. First we give a small introduction to the design of Scikit-Learn and its inner workings. Then we show how easily Scikit-Learn can be extended by creating an own estimator. In order to demonstrate this, we extend Scikit-Learn by the popular and robust Theil-Sen Estimator that is currently not in Scikit-Learn. We also motivate this estimator by outlining some of its superior properties compared to the ordinary least squares method (LinearRegression in Scikit-Learn).
Keywords