learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification

This is the most stable version of learningmachine for R: the one you should use. learningmachine is a package for Machine Learning that includes uncertainty quantification for regression and classification (work in progress), and explainability through sensitivity analysis. So far, it offers a unified interface for:

• lm: Linear model
• bcn: Boosted Configuration ‘neural’ Networks, see https://www.researchgate.net/publication/380760578_Boosted_Configuration_neural_Networks_for_supervised_classification
• extratrees: Extremely Randomized Trees; see https://link.springer.com/article/10.1007/s10994-006-6226-1
• glmnet: Elastic Net Regression; see https://glmnet.stanford.edu/
• krr: Kernel Ridge Regression; see for example https://www.jstatsoft.org/article/view/v079i03
• ranger: Random Forest; see https://www.jstatsoft.org/article/view/v077i01
• ridge: Ridge regression; see https://arxiv.org/pdf/1509.09169
• xgboost: a scalable tree boosting system see https://arxiv.org/abs/1603.02754

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:

https://techtonique.r-universe.dev/learningmachine

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