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SVM and de-normalization, should it be done?

HBorgesHBorges Member Posts: 1 Contributor I
edited November 2018 in Help
Hello.

I have an SVM model for training , and a sample data which are used at an "apply model" block . The main goal is to predict total month sales.
I normalize both data with a "Normalize" block.

The problem is that after normalizing both model and data, I end up having results on the normalized scale.
My question is, what can i do to de-normalize/transform the values in the original sales range?

Example
Assumption:
I am using a normalization between 0 and 1.
The prediction is being made for months where i know the sales values for testing purpose.
OriginalSalesNormalized SalesNormalize PredictionOriginalScalePrediction
6598710.964789
629390.750.7562939
558290.40.5558979
638980.80.6961365
I´m new to data mining and rapidminer therefore if you think i should not de-normalize data i´m open to suggestions.

Answers

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    frasfras Member Posts: 93 Contributor II
    Following process shows how revert a normalisation: 

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="6.0.003">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="6.0.003" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="generate_data" compatibility="6.0.003" expanded="true" height="60" name="Generate Data" width="90" x="112" y="30">
            <parameter key="target_function" value="sum classification"/>
            <parameter key="number_examples" value="1000"/>
          </operator>
          <operator activated="true" class="x_validation" compatibility="6.0.003" expanded="true" height="112" name="Validation" width="90" x="246" y="30">
            <description>A cross-validation evaluating a decision tree model.</description>
            <process expanded="true">
              <operator activated="false" class="decision_tree" compatibility="5.0.000" expanded="true" height="76" name="Decision Tree" width="90" x="45" y="210"/>
              <operator activated="true" class="support_vector_machine" compatibility="6.0.003" expanded="true" height="112" name="SVM" width="90" x="45" y="30"/>
              <operator activated="true" class="rescale_confidences" compatibility="6.0.003" expanded="true" height="76" name="Rescale Confidences" width="90" x="246" y="30"/>
              <connect from_port="training" to_op="SVM" to_port="training set"/>
              <connect from_op="SVM" from_port="model" to_op="Rescale Confidences" to_port="prediction model"/>
              <connect from_op="SVM" from_port="exampleSet" to_op="Rescale Confidences" to_port="example set"/>
              <connect from_op="Rescale Confidences" 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="5.0.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="5.0.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
              <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="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>
          <connect from_op="Generate Data" from_port="output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="model" to_port="result 2"/>
          <connect from_op="Validation" from_port="averagable 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"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
    </process>


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