Visualize Model by SOM (RapidMiner Studio Core)
SynopsisThis operator generates a SOM plot (by transforming arbitrary number of dimensions to two) of the given ExampleSet and colorizes the landscape with the predictions of the given model.
The Visualize Model by SOM operator provides the visualization of arbitrary models with help of the dimensionality reduction via SOM of both the data set and the given model. A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by Teuvo Kohonen, and is sometimes called a Kohonen map.
- example set input (Data Table)
This input port expects an ExampleSet. It is the output of the Decision Tree operator in the attached Example Process. The output of other operators can also be used as input.
- model (Model)
This input port expects a model. It is the output of the Decision Tree operator in the attached Example Process. The output of other operators can also be used as input.
- example set (Data Table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
- model (Model)
The model that was given as input is passed without changing to the output through this port. This is usually used to reuse the same model in further operators or to view the model in the Results Workspace.
- visualization (null)
The SOM visualization is returned through this port.
Visualizing the Decision Tree by SOM
The 'Iris' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can have a look at the ExampleSet. The Decision Tree operator is applied on this ExampleSet and the resultant model is provided as input model to the Visualize Model by SOM operator. The original ExampleSet is also provided as input. The ExampleSet, model and visualization returned by the Visualize Model by SOM operator are connected to the output and can be viewed in the Results Workspace.