Apply Threshold (RapidMiner Studio Core)

Synopsis

This operator applies a threshold on soft classified data.

Description

The Apply Threshold operator applies the given threshold to a labeled ExampleSet and maps a soft prediction to crisp values. The threshold is provided through the threshold port. Mostly the Create Threshold operator is used for creating thresholds before it is applied using the Apply Threshold operator. If the confidence for the second class is greater than the given threshold the prediction is set to this class otherwise it is set to the other class. This can be easily understood by studying the attached Example Process.

Among various classification methods, there are two main groups of methods: soft and hard classification. In particular, a soft classification rule generally estimates the class conditional probabilities explicitly and then makes the class prediction based on the largest estimated probability. In contrast, hard classification bypasses the requirement of class probability estimation and directly estimates the classification boundary.

Input

example set (Data Table)
This input port expects a labeled ExampleSet. The ExampleSet should have label and prediction attributes as well as attributes for confidence of predictions.
threshold
The threshold is provided through this input port. Frequently, the Create Threshold operator is used for providing threshold at this port.

Output

example set (Data Table)
The predictions of the input ExampleSet are changed according to the threshold given at the threshold port and the modified ExampleSet is delivered through this port.

Tutorial Processes

Creating and Applying thresholds

This Example Process starts with a Subprocess operator. This subprocess provides the labeled ExampleSet. Double-click on the Subprocess operator to see what is happening inside the subprocess although it is not directly relevant to the use of the Apply Threshold operator. In the subprocess, the K-NN classification model is learned and applied on different samples of the 'Weighting' data set. The resultant labeled ExampleSet is output of this subprocess. A breakpoint is inserted after this subprocess so that you can have a look at the labeled ExampleSet before the application of the Apply Threshold operator. You can see that the ExampleSet has 20 examples. 11 of them are predicted as 'positive' and the remaining 9 examples are predicted as 'negative'. If you sort the results according to the confidence of positive prediction, you will easily see that among 11 examples predicted as 'positive', 3 examples have confidence 0.600, 4 examples have confidence 0.700, 3 examples have confidence 0.800 and 1 example has confidence 0.900.

Now let us have a look at what is happening outside the subprocess. The Create Threshold operator is used for creating a threshold. The threshold parameter is set to 0.700 and the first class and second class parameters are set to 'negative' and 'positive' respectively. This threshold is applied on the labeled ExampleSet using the Apply Threshold operator. We know that when the Apply Threshold operator is applied on an ExampleSet, if the confidence for the second class is greater than the given threshold then the prediction is set to this class otherwise it is set to the other class. In this process, if the confidence for the second class i.e. 'positive' (class specified in the second class parameter of the Create Threshold operator) is greater than the given threshold i.e. 0.700 (threshold specified in the threshold parameter of the Create Threshold operator) the prediction is set to 'positive' otherwise it is set to 'negative'. In the labeled ExampleSet only 4 examples had confidence (positive) greater than 0.700. When the Apply Threshold operator is applied only these 4 examples are assigned 'positive' prediction and all other examples are assigned 'negative' predictions.