PrologMethods/Classification/decision_tree.md

+# Decision tree
+
+An implementation of an ID3-style decision tree for classification.
+
+# Available Predicates
+
+* [initModel/9](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#initmodel9)
+* [classifyPoint/5](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifypoint5)
+* [classifyMatrix/7](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifymatrix7)
+* [train/10](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#train10)
+
+---
+
+[links/resources](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#connected-linksresources)
+
+## **_initModel/9_**
+
+Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.
+
+Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
+
+```prolog
+%% part of the predicate definition
+initModel(  +pointer(float_array), +integer, +integer,
+            +pointer(float_array), +integer,
+            +integer, +integer, +float32, +integer).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| dataset | +matrix | Training dataset | - |
+| labels | +vector | Training labels. | - |
+| numClasses | +integer | Number of classes in the dataset. | - |
+| minimumLeafSize | +integer | Minimum number of points in each leaf node. | 20 |
+| minimumGainSplit | +float | Minimum gain for node splitting. | 1e-7 |
+| maximumDepth | +integer | Maximum depth of the tree (0 means no limit). | 0 |
+
+---
+
+## **_classifyPoint/5_**
+
+Classify the given point and also return estimates of the probability for each class in the given vector.
+
+```prolog
+%% part of the predicate definition
+classifyPoint(  +pointer(float_array), +integer,
+                -integer,
+                -pointer(float_array), -integer).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| point | +vector | Point to classify. | - |
+| prediction | -integer | This will be set to the predicted class of the point. | - |
+| probabilities | -vector | This will be filled with class probabilities for the point. | - |
+
+---
+
+## **_classifyMatrix/7_**
+
+Classify the given points and also return estimates of the probabilities for each class in the given matrix.
+
+```prolog
+%% part of the predicate definition
+classifyMatrix(  +pointer(float_array), +integer, +integer,
+                 -pointer(float_array), -integer,
+                 -pointer(float_array), -integer).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| data | +matrix | Set of points to classify. | - |
+| predictions | -vector | This will be filled with predictions for each point. | - |
+| probabilities | -matrix | This will be filled with class probabilities for each point. | - |
+
+---
+
+## **_train/10_**
+
+Train the decision tree on the given data, assuming that all dimensions are numeric.
+
+This will overwrite the given model. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
+
+```prolog
+%% part of the predicate definition
+train(  +pointer(float_array), +integer, +integer,
+        +pointer(float_array), +integer,
+        +integer, +integer, +float32, +integer,
+        [-float32]).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| dataset | +matrix | Training dataset | - |
+| labels | +vector | Training labels. | - |
+| numClasses | +integer | Number of classes in the dataset. | - |
+| minimumLeafSize | +integer | Minimum number of points in each leaf node. | 20 |
+| minimumGainSplit | +float | Minimum gain for node splitting. | 1e-7 |
+| maximumDepth | +integer | Maximum depth of the tree (0 means no limit). | 0 |
+| entropy | -float | The final entropy of decision tree. | - |
+
+---
+
+# Connected Links/Resources
+
+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)
+
+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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