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How to predict using support vector machine

fatinharun94fatinharun94 Member Posts: 3 Contributor I
edited November 2018 in Help

Hi. I am new here. I have dengue outbreak and 9 attributes of weather data. I am working on a project to predict dengue outbreak based on the weather data by using support vector machine. But I don't know the step to make this happen. I am very grateful if you can help me. Thank you.

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Answers

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

    The first question I have is do you have a label for your data set. My guess is that it would be "outbreak" and "no outbreak."  

     

    The bigger question is if there is a time series componment to all this, for example is a series of days with temperature over 70F somehow correlated with an outbreak?

    <?xml version="1.0" encoding="UTF-8"?><process version="7.4.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="7.4.000" expanded="true" name="Process">
    <process expanded="true">
    <operator activated="true" class="generate_data" compatibility="7.4.000" expanded="true" height="68" name="Generate Data" width="90" x="45" y="34">
    <parameter key="target_function" value="random classification"/>
    <parameter key="number_of_attributes" value="9"/>
    <description align="center" color="transparent" colored="false" width="126">Type your comment</description>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation" width="90" x="246" y="34">
    <parameter key="sampling_type" value="stratified sampling"/>
    <process expanded="true">
    <operator activated="true" class="support_vector_machine" compatibility="7.4.000" expanded="true" height="124" name="SVM" width="90" x="204" y="34">
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="0.001"/>
    <parameter key="C" value="1000.0"/>
    </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"/>
    <description align="left" color="green" colored="true" height="80" resized="true" width="248" x="140" y="215">In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)</description>
    </process>
    <process expanded="true">
    <operator activated="true" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance" compatibility="7.4.000" 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="103" resized="true" width="315" x="38" y="137">The model created in the Training step is applied to the current test set (10 %).&lt;br/&gt;The performance is evaluated and sent to the operator results.</description>
    </process>
    <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a SVM model.</description>
    </operator>
    <connect from_op="Generate Data" from_port="output" to_op="Validation" to_port="example set"/>
    <connect from_op="Validation" from_port="performance 1" to_port="result 1"/>
    <portSpacing port="source_input 1" spacing="0"/>
    <portSpacing port="sink_result 1" spacing="0"/>
    <portSpacing port="sink_result 2" spacing="0"/>
    </process>
    </operator>
    </process>
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