Classification Tree

Classification Tree

Signals

Inputs:

• Data

  A data set

• Preprocessor

  Preprocessed data.

Outputs:

• Learner

  A classification tree learning algorithm with settings as specified in the dialog.

• Classification Tree

  A trained classifier (a subtype of Classifier). The signal Classification Tree sends data only if the learning data (signal Classified Data) is present.

Description

Classification Tree is a simple classification algorithm that splits the data into nodes by class purity. It is a precursor to Random Forest. Classification Tree in Orange is designed in-house and can handle both discrete and continuous data sets.

1. The learner can be given a name under which it will appear in other widgets. The default name is “Classification Tree”.
2. Tree parameters: - Induce binary tree: build a binary tree (split into two child nodes) - Min. number of instances in leaves: if checked, the algorithm will never construct a split which would put less than the specified number of training examples into any of the branches. - Do not split subsets smaller than: forbids the algorithm to split the nodes with less than the given number of instances. - Stop when majority reaches [%]: stop splitting the nodes after a specified majority threshold is reached - Limit the maximal tree depth: limits the depth of the classification tree to the specified number of node levels.
3. Produce a report. After changing the settings, you need to click Apply, which will put the new learner in the output and, if the training examples are given, construct a new classifier and output it as well. Alternatively, tick the box on the left and changes will be communicated automatically.

Examples

There are two typical uses for this widget. First, you may want to induce a model and check what it looks like. You do it with the schema below; to learn more about it, see the documentation on Tree Viewer.

The second schema checks the nodes of the built tree.

We used the Iris data set in both examples.