PrologMethods/Classification/decision_tree.md

@@ -109,11 +109,11 @@ train(  +pointer(float_array), +integer, +integer,
 
 If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
 
-* [MLpack::decision_tree_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1tree_1_1DecisionTree.html)
-* [MLpack::decision_tree_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#decision_tree)
+* [**MLpack::decision_tree_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1tree_1_1DecisionTree.html)
+* [**MLpack::decision_tree_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#decision_tree)
 
 added some of the links from the python documentation
 
-* Random forest
-* [Decision trees on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning)
-* [Induction of Decision Trees (pdf)](https://link.springer.com/content/pdf/10.1007/BF00116251.pdf)
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+* [**random_forest**](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/random_forest)
+* [**Decision trees on Wikipedia**](https://en.wikipedia.org/wiki/Decision_tree_learning)
+* [**Induction of Decision Trees (pdf)**](https://link.springer.com/content/pdf/10.1007/BF00116251.pdf)
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