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Machine Learning with BayesiaLab

BayesiaLab features a comprehensive array of highly optimized learning algorithms that can quickly uncover structures in datasets. The optimization criteria in BayesiaLab’s learning algorithms are based on information theory (e.g. the Minimum Description Length). With that, no assumptions regarding the variable distributions are made. These algorithms can be used for all kinds and all sizes of problem domains, sometimes including thousands of variables with millions of potentially relevant relationships.

Unsupervised Structural Learning

In statistics, “unsupervised learning” is typically understood to be a classification or clustering task. To make a very clear distinction, we place emphasis on “structural” in “Unsupervised Structural Learning,” which covers a number of important algorithms in BayesiaLab.

Unsupervised Structural Learning means that BayesiaLab can discover probabilistic relationships between a large number of variables, without having to specify input or output nodes. One might say that this is a quintessential form of knowledge discovery, as no assumptions are required to perform these algorithms on unknown datasets.

The following screen capture shows BayesiaLab machine-learning a network from the S&P 500 dataset (see also Chapter 7 in our book, Bayesian Networks & BayesiaLab). This example is also the subject of a Quick Start Demo on Knowledge Discovery.

 

With the release of BayesiaLab 7, such large networks can also be visualized in three dimensions with the 3D Mapping function.