Welcome to Scikit-ELM’s documentation!

Extreme Learning Machine classifier and regressor toolbox with Scikit-Learn compatibility.

Extreme Learning Machine (ELM) is a general purpose regression and classification method with computationally efficient formulation, featuring great performance on a wide range on problems. It scales to the largets datasets or model sizes, provides a compact neural network model for inference, and easily updates a model with new data.

Scikit-ELM toolbox is compatible with an industry standard Scikit-Learn framework, and can be a part of its analytical pipeline. It accepts data in dense, sparse formats as well as Pandas.DataFrame. Even the largest datasets or models can be analysed with Dask library backend, that reads multiple data files in parallel and runs out-of-memory batch computations. GPU acceleration is supported – including on Mac computers with PlaidML library.

Introduction to ELM method

Benefits and use cases of Extreme Learning Machine.

Getting started

Information regarding this template and how to modify it for your own project.

User Guide

An example of narrative documentation.

API Documentation

An example of API documentation.


A set of examples. It complements the User Guide.