Home/Decision-Tree-&-Random-Forrest.md

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+On this page are information on how to use [decision trees](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree) and [random forests](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Random-Forest).
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+## [Decision Tree](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree)
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+This implementation follows the typical binary decision tree algorithm and it is more modular. The following modules are available:
+- [Feature Selection](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree/Feature-Selection)
+- [Impurity Measure](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree/Impurity-Measure)
+- [Leaf Function](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree/Leaf-Function)
+- [Split Algorithm](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree/Split-Algorithm)
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+Of these only split algorithms and impurity measure are necessary for training. Leaf functions will be require when making predictions.
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+## [Random Forests](https://gitlab.ub.uni-giessen.de/gc2052/machine-learning/-/wikis/Home/Decision-Tree-&-Random-Forrest/Decision-Tree)
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+Random Forests are simple collections of decision trees.
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