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Welcome to nnetsauce's website.

nnetsauce does Statistical/Machine Learning (ML) using advanced combinations of randomized and quasi-randomized neural networks layers. It contains models for regression, classification, and time series forecasting. Every ML model in nnetsauce is based on components g(XW + b), where:

  • X is a matrix containing explanatory variables and optional clustering information. Clustering the inputs helps in taking into account data’s heterogeneity before model fitting.
  • W creates new, additional explanatory variables from X. W can be drawn from various random and quasirandom sequences.
  • b is an optional bias parameter.
  • g is an activation function such as the hyperbolic tangent or the sigmoid function, that renders the combination of explanatory variables – through W – nonlinear.

nnetsauce’s source code is available on GitHub.

You can read posts about nnetsauce in this blog, and for current references, feel free consult the section: References.

Looking for a specific function? You can also use the search function available in the navigation bar.

Installing (for Python and R)


  • 1st method: by using pip at the command line for the stable version
pip install nnetsauce
  • 2nd method: from Github, for the development version
pip install git+https://github.com/Techtonique/nnetsauce.git


git clone https://github.com/Techtonique/nnetsauce.git
cd nnetsauce
make install


  • 1st method: From Github, in R console:

General rule for using the package in R: object accesses with .'s are replaced by $'s. See also Quick start.


Examples of use:


The documentation for each model can be found (work in progress) here:


Want to contribute to nnetsauce's development on Github, read this!