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"How to USE Performance Operator on Text Mining Root Process"

MUNISHVIRANGMUNISHVIRANG Member Posts: 9 Contributor II
edited May 2019 in Help
Can you Suggest which performance operator work fine for my text mining project.I'm looking for a performance operator which can give me a measure of accuracy and precision for my classification model.

Below is the XML code:


<operator name="Root" class="Process" expanded="yes">
    <operator name="OperatorChain" class="OperatorChain" expanded="yes">
        <operator name="TextInput" class="TextInput" expanded="no">
            <list key="texts">
              <parameter key="Price" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\PRICE"/>
              <parameter key="Process" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\PROCESS"/>
              <parameter key="Product" value="C:\Documents and Settings\munish.virang\Desktop\SAMPLE_DATA_SET\BARCLAYSBANK\PRODUCT"/>
              <parameter key="Promotion" value="C:\Documents and Settings\munish.virang\Desktop\SAMPLE_DATA_SET\BARCLAYSBANK\PROMOTION"/>
            </list>
            <parameter key="output_word_list" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\words.list"/>
            <list key="namespaces">
            </list>
            <parameter key="create_text_visualizer" value="true"/>
            <operator name="StringTokenizer" class="StringTokenizer">
            </operator>
            <operator name="EnglishStopwordFilter" class="EnglishStopwordFilter">
            </operator>
            <operator name="TokenLengthFilter" class="TokenLengthFilter">
                <parameter key="min_chars" value="3"/>
            </operator>
            <operator name="LovinsStemmer" class="LovinsStemmer">
            </operator>
        </operator>
        <operator name="LibSVMLearner" class="LibSVMLearner">
            <list key="class_weights">
            </list>
            <parameter key="calculate_confidences" value="true"/>
        </operator>
        <operator name="ModelWriter" class="ModelWriter">
            <parameter key="model_file" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\SVM.mod"/>
        </operator>
    </operator>
    <operator name="OperatorChain (2)" class="OperatorChain" expanded="yes">
        <operator name="TextInput (2)" class="TextInput" expanded="no">
            <list key="texts">
              <parameter key="PRODUCT" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\PRODUCT"/>
            </list>
            <parameter key="input_word_list" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\words.list"/>
            <list key="namespaces">
            </list>
            <parameter key="create_text_visualizer" value="true"/>
            <operator name="StringTokenizer (2)" class="StringTokenizer">
            </operator>
            <operator name="EnglishStopwordFilter (2)" class="EnglishStopwordFilter">
            </operator>
            <operator name="TokenLengthFilter (2)" class="TokenLengthFilter">
                <parameter key="min_chars" value="3"/>
            </operator>
            <operator name="LovinsStemmer (2)" class="LovinsStemmer">
            </operator>
        </operator>
        <operator name="ModelLoader" class="ModelLoader">
            <parameter key="model_file" value="C:\Documents and Settings\munish.virang\Desktop\XMX\BARCLAYSBANK\SVM.mod"/>
        </operator>
        <operator name="ModelApplier" class="ModelApplier">
            <list key="application_parameters">
            </list>
        </operator>
    </operator>
</operator>

Answers

  • Options
    fischerfischer Member Posts: 439 Maven
    Hi,

    why can't you use one of the regular performance evaluators? Check ClassificationPerformance, BinominalPerformance, Performance, and PerformanceEvaluator.

    Besides, I am not sure why you are choosing such a complicated process setup. There is no need to save your model if all you want to do is apply it on a different data set later. You probably also want to wrap your evaluation into a XValidation. Have a look at the samples.

    Best,
    Simon
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