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Author Topic: Validate data using historical data  (Read 105 times)
aiguti
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Posts: 1


« on: July 23, 2014, 08:25:14 PM »

Dear felows,

I have one historical data of part weight (peso bruto) and I would like to validate if one sample of parts is within the expected value (peso).
I do not know what is the right Model to be used. I tried LDA, Naive Bayes and others but it did not work.

here is the XML

Code:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.005">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.005" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="read_csv" compatibility="5.3.005" expanded="true" height="60" name="Read CSV" width="90" x="45" y="30">
        <parameter key="csv_file" value="C:\Users\aiguti\Documents\kdd\peso - training.csv"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <parameter key="encoding" value="windows-1252"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="Ecode.true.polynominal.attribute"/>
          <parameter key="1" value="Peso Bruto.true.integer.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="read_csv" compatibility="5.3.005" expanded="true" height="60" name="Read CSV (2)" width="90" x="45" y="255">
        <parameter key="csv_file" value="C:\Users\aiguti\Documents\kdd\peso - scoring.csv"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <parameter key="encoding" value="windows-1252"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="ecode.true.polynominal.attribute"/>
          <parameter key="1" value="Peso.true.integer.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.3.005" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
        <parameter key="name" value="Peso Bruto"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="vector_linear_regression" compatibility="5.3.005" expanded="true" height="76" name="Vector Linear Regression" width="90" x="380" y="30"/>
      <operator activated="true" class="apply_model" compatibility="5.3.005" expanded="true" height="76" name="Apply Model" width="90" x="648" y="30">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Read CSV" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Read CSV (2)" from_port="output" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Vector Linear Regression" to_port="training set"/>
      <connect from_op="Vector Linear Regression" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <connect from_op="Apply Model" from_port="model" 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>

Thank you
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homburg
Administrator
Jr. Member
*****
Posts: 95


« Reply #1 on: August 04, 2014, 02:02:14 PM »

Hi aiguti,

with your process you train a model using a dataset called peso-training and later apply it to peso-scoring. So far this looks like a typical holdout strategy, you only need to add a "Performance" operator to compute some performance values. In order to recommend a suitable learner it maybe helpful if you could tell me more about your data and what exactly you want to achieve.

Cheers,
Helge
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