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Drop Uncertain Predictions (RapidMiner Studio Core)
SynopsisThis operator sets all predictions to 'unknown' (missing value) if the corresponding confidence is less than the specified minimum confidence. This operator is used for dropping predictions with low confidence values.
The Drop Uncertain Predictions operator expects a labeled ExampleSet i.e. an ExampleSet with label and prediction attributes along with prediction confidences. The minimum confidence threshold is specified through the min confidence parameter. All those predictions of the given ExampleSet are dropped where the corresponding prediction confidence is below the specified threshold. Suppose an ExampleSet with two possible classes 'positive' and 'negative'. If the min confidence parameter is set to 0.700, all the examples that were predicted as 'positive' but their corresponding 'confidence (positive)' value is less than 0.700 are classified as missing values. Similarly the label value is set to missing value for all those examples that were predicted as 'negative' but their corresponding confidence '(negative)' value is less than 0.700. This operator also allows you to define different minimum confidence thresholds for different classes through the min confidences parameter.
- example set input (IOObject)
This input port expects a labeled ExampleSet. It is the output of the Apply Model operator in the attached Example Process. The output of other operators can also be used as input if it is a labeled ExampleSet.
- example set output (IOObject)
The uncertain predictions are dropped and the resultant ExampleSet is delivered through this port.
- original (IOObject)
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.
- class_handlingThis parameter specifies the mode of class handling which defines if all classes are handled equally or if individual class thresholds are set.
- balanced: In this case all classes are handled equally i.e. the same confidence threshold is applied on all possible values of the label. The minimum confidence threshold is specified through the min confidence parameter.
- unbalanced: In this case classes are not handled equally i.e. different confidence thresholds can be specified for different classes through the min confidences parameter.
- min_confidenceThis parameter is only available when the class handling parameter is set to 'balanced'. This parameter sets the minimum confidence threshold for all the classes. Predictions below this confidence will be dropped. Range: real
- min_confidencesThis parameter is only available when the class handling parameter is set to 'unbalanced'. This parameter specifies individual thresholds for classes. Predictions below these confidences will be dropped. Range: list
Dropping uncertain predictions of the Naive Bayes operator
The 'Golf' data set is loaded using the Retrieve operator. The Naive Bayes operator is applied on it to generate a classification model. The resultant classification model is applied on the 'Golf-Testset' data set by using the Apply Model operator. A breakpoint is inserted here so that you can see the labeled ExampleSet generated by the Apply Model operator. You can see that 10 examples have been classified as 'yes' but only 6 of them have 'confidence (yes)' above 0.700. Only 2 examples have been classified as 'no' but only 1 of them has 'confidence (no)' above 0.700. This labeled ExampleSet is provided to the Drop Uncertain Predictions operator. The min confidence parameter is set to 0.7. Thus all the examples where the prediction confidence is below 0.7 are set to missing values. This can be seen in the Results Workspace. 7 examples had a prediction confidence below 0.7 and all of them have been dropped.