Created Trees (markdown)

Trees.md

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+**See also: [`examples/trees/trees.go`](https://github.com/sjwhitworth/golearn/blob/master/examples/trees/trees.go).**
+
+##ID3 
+ID3 is a decision tree induction algorithm which splits on the Attribute which gives the greatest Information Gain (entropy gradient). It  performs well on categorical data. Numeric datasets will need to be discretised before using ID3 (see [Filtering](Filtering)).
+
+##Random Trees
+Random Trees are structurally identical to those generated by ID3, but the split Attribute is chosen randomly. Golearn's implementation allows you to choose up to *k* nodes for consideration at each split. 
+
+##Random forests
+Random forests are a bagged ensemble technique which combines multiple Random Trees and outputs a classification via a majority vote.