This open source project is for Mathematica implementations of statistical and machine learning algorithms that can be used for data analysis, prediction, and recommendation systems.
All code files and executable documents are with the license GPL 3.0. For details see http://www.gnu.org/licenses/ .
All documents are with the license Creative Commons Attribution 4.0 International (CC BY 4.0). For details see https://creativecommons.org/licenses/by/4.0/ .
The algorithms implementations are given in Mathematica package files (".m"). Explanations or presentations about the algorithms are given in Mathematica notebook files (".nb").
Here are some fairly unique to the Mathematica landscape algorithms:
- mosaic plots;
- outlier identifiers;
- associative rules finding;
- prefix trees (tries);
- quantile regression;
- Chernoff faces;
- non-negative matrix factorization;
- independent component analysis;
- receiver operating characteristic;
- classifier ensembles;
- a framework for linear vector space representations of document collections;
- an item-item recommender framework based on sparse linear algebra;
- a naive Bayesian classifiers generator;
- functional parsers.
The implemented algorithms are (usually) well documented. There are also fair amount of documents with related applications.
Some of the algorithms have counterparts implementations in R or other languages.
Associated blog (at WordPress)
There is a blog associated with this project: http://mathematicaforprediction.wordpress.com .
04.07.2013, Florida, USA
11.01.2017, Florida, USA (updated)