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    • Update decision_tree authored by Dean Samuel Schmitz's avatar Dean Samuel Schmitz
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    PrologMethods/Classification/decision_tree.md
    View page @ 02f5d3e3
    ...@@ -2,18 +2,26 @@ ...@@ -2,18 +2,26 @@
    An implementation of an ID3-style decision tree for classification. An implementation of an ID3-style decision tree for classification.
    ```prolog
    :- use_module('path/to/.../src/methods/adaboost/adaboost_test.pl').
    %% usage example
    adaboost_initModelWithTraining([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, [0,0,1,0], 2, perceptron, 50, 0.0001),
    adaboost_classify([3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5], 3, PredictionList, ProbabilitiesList, _).
    ```
    # Available Predicates # Available Predicates
    * [initModel/9](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#initmodel9) * [decision_tree_initModel/7](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_initmodel7)
    * [classifyPoint/5](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifypoint5) * [decision_tree_classifyPoint/3](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_classifypoint3)
    * [classifyMatrix/7](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifymatrix7) * [decision_tree_classifyMatrix/5](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_classifymatrix5)
    * [train/10](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#train10) * [decision_tree_train/8](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_train8)
    --- ---
    [links/resources](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#connected-linksresources) [links/resources](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#connected-linksresources)
    ## **_initModel/9_** ## **_decision_tree_initModel/7_**
    Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type. Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.
    ...@@ -27,6 +35,7 @@ initModel( +pointer(float_array), +integer, +integer, ...@@ -27,6 +35,7 @@ initModel( +pointer(float_array), +integer, +integer,
    ``` ```
    ### Parameters ### Parameters
    | Name | Type | Description | Default | | Name | Type | Description | Default |
    |------|------|-------------|---------| |------|------|-------------|---------|
    | dataset | +matrix | Training dataset | - | | dataset | +matrix | Training dataset | - |
    ...@@ -38,7 +47,7 @@ initModel( +pointer(float_array), +integer, +integer, ...@@ -38,7 +47,7 @@ initModel( +pointer(float_array), +integer, +integer,
    --- ---
    ## **_classifyPoint/5_** ## **_decision_tree_classifyPoint/3_**
    Classify the given point and also return estimates of the probability for each class in the given vector. Classify the given point and also return estimates of the probability for each class in the given vector.
    ...@@ -50,6 +59,7 @@ classifyPoint( +pointer(float_array), +integer, ...@@ -50,6 +59,7 @@ classifyPoint( +pointer(float_array), +integer,
    ``` ```
    ### Parameters ### Parameters
    | Name | Type | Description | Default | | Name | Type | Description | Default |
    |------|------|-------------|---------| |------|------|-------------|---------|
    | point | +vector | Point to classify. | - | | point | +vector | Point to classify. | - |
    ...@@ -58,7 +68,7 @@ classifyPoint( +pointer(float_array), +integer, ...@@ -58,7 +68,7 @@ classifyPoint( +pointer(float_array), +integer,
    --- ---
    ## **_classifyMatrix/7_** ## **_decision_tree_classifyMatrix/5_**
    Classify the given points and also return estimates of the probabilities for each class in the given matrix. Classify the given points and also return estimates of the probabilities for each class in the given matrix.
    ...@@ -70,6 +80,7 @@ classifyMatrix( +pointer(float_array), +integer, +integer, ...@@ -70,6 +80,7 @@ classifyMatrix( +pointer(float_array), +integer, +integer,
    ``` ```
    ### Parameters ### Parameters
    | Name | Type | Description | Default | | Name | Type | Description | Default |
    |------|------|-------------|---------| |------|------|-------------|---------|
    | data | +matrix | Set of points to classify. | - | | data | +matrix | Set of points to classify. | - |
    ...@@ -78,7 +89,7 @@ classifyMatrix( +pointer(float_array), +integer, +integer, ...@@ -78,7 +89,7 @@ classifyMatrix( +pointer(float_array), +integer, +integer,
    --- ---
    ## **_train/10_** ## **_decision_tree_train/8_**
    Train the decision tree on the given data, assuming that all dimensions are numeric. Train the decision tree on the given data, assuming that all dimensions are numeric.
    ...@@ -93,6 +104,7 @@ train( +pointer(float_array), +integer, +integer, ...@@ -93,6 +104,7 @@ train( +pointer(float_array), +integer, +integer,
    ``` ```
    ### Parameters ### Parameters
    | Name | Type | Description | Default | | Name | Type | Description | Default |
    |------|------|-------------|---------| |------|------|-------------|---------|
    | dataset | +matrix | Training dataset | - | | dataset | +matrix | Training dataset | - |
    ...@@ -109,8 +121,8 @@ train( +pointer(float_array), +integer, +integer, ...@@ -109,8 +121,8 @@ 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. 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_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/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_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#decision_tree)
    added some of the links from the python documentation added some of the links from the python documentation
    ......