Regression Tree =================== .. figure:: icons/regression-tree.png Regression Tree Signals ------- **Inputs**: - **Data** A data set - **Preprocessor** Preprocessed data. **Outputs**: - **Learner** A regression tree learning algorithm with settings as specified in the dialog. - **Predictor** Trained regressor. Description ----------- .. figure:: images/RegressionTree-stamped.png 1. The learner can be given a name under which it will appear in other widgets. The default name is "Regression Tree". 2. In *Feature selection*, there is just one option, namely `Mean Squared Error `_, which measures the average of the squares of the errors or deviations (the difference between the estimator and what is estimated). 3. *Pruning* criteria: - **Minimal 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. - **Stop splitting nodes with less instances than** forbids the algorithm to split the nodes with less than the given number of instances. - **Limit the depth** of the regression tree. 3. Produce a report. 4. 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 regressor and output it as well. 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 workflow below. To learn more about it, see the documentation on :doc:`Regression Tree Viewer <../regression/regressiontreeviewer>`. .. figure:: images/Regression-Tree-Example1.png The second schema checks the accuracy of the algorithm. The selected tree node is presented in the :doc:`Scatter Plot <../visualize/scatterplot>` and we can see that the selected examples exhibit the same features. .. figure:: images/Regression-Tree-Example2.png