Random Tree


Synopsis

Learns a single decision tree. For each split only a random subset of attributes is available.


Description

This operator learns decision trees from both nominal and numerical data. Decision trees are powerful classification methods which often can also easily be understood. The random tree learner works similar to Quinlan's C4.5 or CART but it selects a random subset of attributes before it is applied. The size of the subset is defined by the parameter subset_ratio.


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ExampleProcess