Move Stacking documentation

Author: ajdapretnar

doc/visual-programming/source/index.rst

@@ -96,6 +96,7 @@ Model
    widgets/model/adaboost
    widgets/model/neuralnetwork
    widgets/model/stochasticgradient
+   widgets/model/stacking
    widgets/model/loadmodel
    widgets/model/savemodel
 

doc/visual-programming/source/widgets/model/icons/stacking.png

@@ -0,0 +1,40 @@
+Stacking
+========
+
+Stack multiple models.
+
+Inputs
+    Data
+        input dataset
+    Preprocessor
+        preprocessing method(s)
+    Learners
+        learning algorithm
+    Aggregate
+        model aggregation method
+
+Outputs
+    Learner
+        aggregated (stacked) learning algorithm
+    Model
+        trained model
+
+
+**Stacking** is an ensemble method that computes a meta model from several base models. The **Stacking** widget has the **Aggregate** input, which provides a method for aggregating the input models. If no aggregation input is given the default methods are used. Those are **Logistic Regression** for classification and **Ridge Regression** for regression problems.
+
+.. figure:: images/Stacking-stamped.png
+   :scale: 50%
+
+1. The meta learner can be given a name under which it will appear in other widgets. The default name is “Stack”.
+2. Click *Apply* to commit the aggregated model. That will put the new learner in the output and, if the training examples are given, construct a new model and output it as well. To communicate changes automatically tick *Apply Automatically*.
+3. Access help and produce a report.
+
+Example
+-------
+
+We will use **Paint Data** to demonstrate how the widget is used. We painted a complex dataset with 4 class labels and sent it to **Test & Score**. We also provided three **kNN** learners, each with a different parameters (number of neighbors is 5, 10 or 15). Evaluation results are good, but can we do better? 
+
+Let's use **Stacking**. **Stacking** requires several learners on the input and an aggregation method. In our case, this is **Logistic Regression**. A constructed meta learner is then sent to **Test & Score**. Results have improved, even if only marginally. **Stacking** normally works well on complex data sets.
+
+.. figure:: images/Stacking-Example.png
+