RapidMiner 9.7 is Now Available

Lots of amazing new improvements including true version control! Learn more about what's new here.

CLICK HERE TO DOWNLOAD

Sensitivity Analysis for Predictive Model

jing_majing_ma Member Posts: 2 Contributor I
edited November 2018 in Help

I have a question regarding sensitivity analysis for the resulted predictive model. For example, after data mining, I built a model from Naive Bayes. Is there any way readily in Rapid Miner to give the sensitivity results of parameters in the model from Bayes? Thanks so much!

Answers

  • Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761   Unicorn

    Of course you can do that in RapidMiner. My colleagu

    @mschmitz designed a few processes that help you get the sensitivity of variables.

     

    Here's one way. 

    <?xml version="1.0" encoding="UTF-8"?><process version="7.4.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
    <operator activated="true" class="retrieve" compatibility="7.4.000" expanded="true" height="68" name="Retrieve" width="90" x="45" y="30">
    <parameter key="repository_entry" value="//Samples/data/Iris"/>
    </operator>
    <operator activated="true" class="optimize_selection_forward" compatibility="7.4.000" expanded="true" height="103" name="Forward Selection" width="90" x="180" y="30">
    <parameter key="maximal_number_of_attributes" value="33"/>
    <parameter key="speculative_rounds" value="55"/>
    <process expanded="true">
    <operator activated="true" class="x_validation" compatibility="7.4.000" expanded="true" height="112" name="InsV" width="90" x="45" y="30">
    <process expanded="true">
    <operator activated="true" class="weka:W-J48" compatibility="7.3.000" expanded="true" height="76" name="W-J48" width="90" x="45" y="30"/>
    <connect from_port="training" to_op="W-J48" to_port="training set"/>
    <connect from_op="W-J48" from_port="model" to_port="model"/>
    <portSpacing port="source_training" spacing="0"/>
    <portSpacing port="sink_model" spacing="0"/>
    <portSpacing port="sink_through 1" spacing="0"/>
    </process>
    <process expanded="true">
    <operator activated="true" class="apply_model" compatibility="7.1.001" expanded="true" height="76" name="InsA" width="90" x="45" y="30">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="7.4.000" expanded="true" height="76" name="Performance (2)" width="90" x="179" y="30">
    <parameter key="accuracy" value="false"/>
    <parameter key="kappa" value="true"/>
    <list key="class_weights"/>
    </operator>
    <connect from_port="model" to_op="InsA" to_port="model"/>
    <connect from_port="test set" to_op="InsA" to_port="unlabelled data"/>
    <connect from_op="InsA" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
    <connect from_op="Performance (2)" from_port="performance" to_port="averagable 1"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_averagable 1" spacing="0"/>
    <portSpacing port="sink_averagable 2" spacing="0"/>
    </process>
    </operator>
    <operator activated="true" class="log" compatibility="7.4.000" expanded="true" height="76" name="Log" width="90" x="180" y="30">
    <list key="log">
    <parameter key="feature" value="operator.Forward Selection.value.feature_names"/>
    <parameter key="performance" value="operator.InsV.value.performance"/>
    <parameter key="deviation" value="operator.InsV.value.deviation"/>
    <parameter key="cpu time" value="operator.InsV.value.cpu-execution-time"/>
    <parameter key="apply count" value="operator.InsV.value.applycount"/>
    <parameter key="number of attributes" value="operator.Forward Selection.value.number of attributes"/>
    </list>
    </operator>
    <connect from_port="example set" to_op="InsV" to_port="training"/>
    <connect from_op="InsV" from_port="averagable 1" to_op="Log" to_port="through 1"/>
    <connect from_op="Log" from_port="through 1" to_port="performance"/>
    <portSpacing port="source_example set" spacing="0"/>
    <portSpacing port="sink_performance" spacing="0"/>
    </process>
    </operator>
    <operator activated="true" class="select_by_weights" compatibility="7.4.000" expanded="true" height="103" name="Select by Weights" width="90" x="313" y="30"/>
    <operator activated="true" class="x_validation" compatibility="7.4.000" expanded="true" height="124" name="Validation" width="90" x="447" y="34">
    <process expanded="true">
    <operator activated="true" class="weka:W-J48" compatibility="7.3.000" expanded="true" height="76" name="W-J48 (2)" width="90" x="45" y="30"/>
    <connect from_port="training" to_op="W-J48 (2)" to_port="training set"/>
    <connect from_op="W-J48 (2)" from_port="model" to_port="model"/>
    <portSpacing port="source_training" spacing="0"/>
    <portSpacing port="sink_model" spacing="0"/>
    <portSpacing port="sink_through 1" spacing="0"/>
    </process>
    <process expanded="true">
    <operator activated="true" class="apply_model" compatibility="7.1.001" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="7.4.000" expanded="true" height="76" name="Performance (3)" width="90" x="179" y="165">
    <parameter key="accuracy" value="false"/>
    <parameter key="kappa" value="true"/>
    <list key="class_weights"/>
    </operator>
    <connect from_port="model" to_op="Apply Model" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
    <connect from_op="Apply Model" from_port="labelled data" to_op="Performance (3)" to_port="labelled data"/>
    <connect from_op="Performance (3)" from_port="performance" to_port="averagable 1"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_averagable 1" spacing="0"/>
    <portSpacing port="sink_averagable 2" spacing="0"/>
    </process>
    </operator>
    <operator activated="true" class="log_to_data" compatibility="7.4.000" expanded="true" height="103" name="Log to Data" width="90" x="447" y="210">
    <parameter key="log_name" value="Log"/>
    </operator>
    <connect from_op="Retrieve" from_port="output" to_op="Forward Selection" to_port="example set"/>
    <connect from_op="Forward Selection" from_port="example set" to_op="Select by Weights" to_port="example set input"/>
    <connect from_op="Forward Selection" from_port="attribute weights" to_op="Select by Weights" to_port="weights"/>
    <connect from_op="Select by Weights" from_port="example set output" to_op="Validation" to_port="training"/>
    <connect from_op="Select by Weights" from_port="original" to_op="Log to Data" to_port="through 1"/>
    <connect from_op="Select by Weights" from_port="weights" to_port="result 3"/>
    <connect from_op="Validation" from_port="model" to_port="result 1"/>
    <connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
    <connect from_op="Log to Data" from_port="exampleSet" to_port="result 4"/>
    <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"/>
    <portSpacing port="sink_result 4" spacing="0"/>
    <portSpacing port="sink_result 5" spacing="0"/>
    </process>
    </operator>
    </process>

     

Sign In or Register to comment.