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The most common task in data analysis is to find relationships of a set of input parameters to one or more output parameters. This kind of analysis is called supervised because the goal of the analysis is given by the user. In the case that no explicit goal parameter is available, the analysis is called unsupervised. The machine learning framework package offers a wide variety of methods for supervised and unsupervised analysis of data.

The following examples describe several methods and scenarios in which supervised or unsupervised analysis can help to understand the underlying structure of your data. These methods can also be used to create computational models from your data sources.

Supervised Analysis
Demonstrates the general tasks of supervised analysis, including how to create a decision tree and a set of rules to describe your data and use these rules to forecast the classification of new data.

Unsupervised Analysis
Describes several methods available for unsupervised learning, including how self-organizing maps (SOMs) can be computed, how different clustering methods can be used to find regions of similarity in the data, and how supervised learning methods are applied to create descriptions for the clusters found.

Shows how a one- and a two-dimensional function can be approximated using various supervised learning methods, including SOM, decision tree, rule based, and RENO.

Note: If you do not own a copy of Mathematica, you will need to download Wolfram CDF Player in order to view these examples.