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

Support vector machine regression LibSVM and PSO

ismail_hdoufaneismail_hdoufane Member Posts: 1 Learner I
edited December 2018 in Help
Dear Community,

 

I am a new user. I have an xls file which contains 159 rows and 13 columns. 

1st column contains values of biological activity (Y) and the others contain(molecular descriptors)  (Xi)

I would like to do Regression for building model using Support Vector Machine LibSVM and PSO.

Best Regards,

Ismail

Answers

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

    You will need a Read Excel, Set Role, Cross Validation, LibSVM, Apply Model and Performance operators as a starting point for your process. Something like this below:

     

    <?xml version="1.0" encoding="UTF-8"?><process version="7.6.002">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="7.6.002" expanded="true" name="Process">
    <process expanded="true">
    <operator activated="true" class="read_excel" compatibility="7.6.002" expanded="true" height="68" name="Read Excel" width="90" x="112" y="34">
    <list key="annotations"/>
    <list key="data_set_meta_data_information"/>
    </operator>
    <operator activated="true" class="set_role" compatibility="7.6.002" expanded="true" height="82" name="Set Role" width="90" x="246" y="34">
    <list key="set_additional_roles"/>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="7.6.002" expanded="true" height="145" name="Validation" width="90" x="380" y="34">
    <parameter key="sampling_type" value="shuffled sampling"/>
    <process expanded="true">
    <operator activated="true" class="support_vector_machine_libsvm" compatibility="7.6.002" expanded="true" height="82" name="SVM" width="90" x="227" y="34">
    <parameter key="svm_type" value="epsilon-SVR"/>
    <list key="class_weights"/>
    </operator>
    <connect from_port="training set" to_op="SVM" to_port="training set"/>
    <connect from_op="SVM" from_port="model" to_port="model"/>
    <portSpacing port="source_training set" 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="7.6.002" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance" compatibility="7.6.002" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
    <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" to_port="labelled data"/>
    <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
    <connect from_op="Performance" from_port="example set" to_port="test set results"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_test set results" spacing="0"/>
    <portSpacing port="sink_performance 1" spacing="0"/>
    <portSpacing port="sink_performance 2" spacing="0"/>
    <description align="left" color="blue" colored="true" height="107" resized="true" width="333" x="28" y="139">Applies the model built from the training data set on the current test set (10 % by default).&lt;br/&gt;The Performance operator calculates performance indicators and sends them to the operator result.</description>
    </process>
    <description align="center" color="transparent" colored="false" width="126">A cross validation including a linear regression.</description>
    </operator>
    <connect from_op="Read Excel" from_port="output" to_op="Set Role" to_port="example set input"/>
    <connect from_op="Set Role" from_port="example set output" to_op="Validation" to_port="example set"/>
    <connect from_op="Validation" from_port="model" to_port="result 1"/>
    <connect from_op="Validation" from_port="performance 1" 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>
    sgenzer
  • SGolbertSGolbert RapidMiner Certified Analyst, Member Posts: 342   Unicorn

    To expand a bit on the topic: what are the big differences between the SVM operator and the LibSVM one? When would you use one instead of the other?

    sgenzer
Sign In or Register to comment.