learningmachine is a package for Machine Learning that includes uncertainty quantification for regression and classification (work in progress), and explainability through sensitivity analysis. This is the most stable version of learningmachine for R: the one you should use (update 2024-07-10: use v2.0.1).

Install learningmachine from GitHub (in R console, tested on macOS, Google Colab, and Posit Cloud)


So far, learningmachine offers a unified interface for:

There are only 2 classes Classifier and Regressor, with methods fit and predict and summary, and all these models can be enhanced by using a quasi-randomized layer that basically augments their capacity. The 3 package vignettes are a great way to get started. Along with the (work in progress, as I’m struggling a little bit with documenting R6 objects) documentation, they’ll eventually be available here:


There are also unit tests in the tests folder on GitHub.


PS: In these slides, in present probabilistic time series forecasting with nnetsauce