Let me know what you think about this feature request and possible work-arounds.
to imply a kind of uncertainty in the model is not really possible. Nevertheless, if it comes to prediction, such uncertainty is indicated by a small confidence. The operator UncertainPredictionsTransformation
allows to mark predictions which have a confidence below a specified thresholds as uncertain
which is indicated by setting the corresponding prediction to missing.
The following process should explain this:
<operator name="Root" class="Process" expanded="yes">
<operator name="ExampleSetGenerator" class="ExampleSetGenerator">
<parameter key="target_function" value="polynomial classification"/>
<operator name="DecisionTree" class="DecisionTree">
<parameter key="keep_example_set" value="true"/>
<operator name="ModelApplier" class="ModelApplier">
<operator name="UncertainPredictionsTransformation" class="UncertainPredictionsTransformation">
<parameter key="min_confidence" value="0.9"/>
Maybe this is helpful for now. Concerning the decision tree uncertainty I am not sure whether it is of great help to add such an uncertainty indicator in the models itself, as in a descriptive setting this model can easily be interpreted by the user. A purity parameter might potentially be helpful to control fitting of the tree, but at the moment we are really busy and have really few time. But maybe we will discuss that issue in the long term. For now you have to use the other parameter to control how far the tree tries to generalize or to exactly fit the data.