Building high-precision classifier with RapidMiner - neural-network

Question

Supposedly, I need to build classifier with high precision and low recall. Which operator should I use in RapidMiner? For example, ApplyModel operator doesn't support any intervention in threshold? It's also unclear how the same ApplyModel operator works with models of different modeling: NeuralNetworks, SVN, etc.

Answer 1

You could use the Select Recall operator. This finds the threshold needed to get the recall you choose.
The input to this operator is a labelled example set containing confidences, the output is a threshold that is applied to the labelled data using the Apply Threshold operator.
The Apply Model operator requires an example set and a model as input.
I've made a simple example that illustrates all of this.
<?xml version="1.0" encoding="UTF-8"?><process version="7.2.003">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.2.003" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="7.2.003" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="34">
<parameter key="repository_entry" value="//Samples/data/Sonar"/>
</operator>
<operator activated="true" class="naive_bayes" compatibility="7.2.003" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="289"/>
<operator activated="true" class="apply_model" compatibility="7.2.003" expanded="true" height="82" name="Apply Model" width="90" x="179" y="289">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="multiply" compatibility="7.2.003" expanded="true" height="103" name="Multiply" width="90" x="246" y="136"/>
<operator activated="true" class="materialize_data" compatibility="7.2.003" expanded="true" height="82" name="Materialize Data" width="90" x="380" y="136"/>
<operator activated="true" class="select_recall" compatibility="7.2.003" expanded="true" height="82" name="Select Recall" width="90" x="313" y="34">
<parameter key="min_recall" value="0.1"/>
</operator>
<operator activated="true" class="apply_threshold" compatibility="7.2.003" expanded="true" height="82" name="Apply Threshold" width="90" x="447" y="34"/>
<operator activated="true" class="performance_binominal_classification" compatibility="7.2.003" expanded="true" height="82" name="Performance" width="90" x="581" y="34"/>
<operator activated="true" class="performance_binominal_classification" compatibility="7.2.003" expanded="true" height="82" name="Performance (2)" width="90" x="581" y="136"/>
<connect from_op="Retrieve Sonar" from_port="output" to_op="Naive Bayes" to_port="training set"/>
<connect from_op="Naive Bayes" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Naive Bayes" from_port="exampleSet" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Select Recall" to_port="example set"/>
<connect from_op="Multiply" from_port="output 2" to_op="Materialize Data" to_port="example set input"/>
<connect from_op="Materialize Data" from_port="example set output" to_op="Performance (2)" to_port="labelled data"/>
<connect from_op="Select Recall" from_port="example set" to_op="Apply Threshold" to_port="example set"/>
<connect from_op="Select Recall" from_port="threshold" to_op="Apply Threshold" to_port="threshold"/>
<connect from_op="Apply Threshold" from_port="example set" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="result 1"/>
<connect from_op="Performance (2)" from_port="performance" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
Hope that helps as a start.