1

I have an example set in rapid miner.It has 2 columns. for Example

colA  colB 
a     1
a     2
b     3
b     2

=====

I have used naive Bayes. It gives probability for each of colB for colA in distribution table. for example, P(2) = .5

I need that distribution table output. 
write model, excel csv, write does not help.

What should I do ? Thanks in advance.

Sazzad
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2 Answers2

1

The simplest solution would just mark the table with you mouse (Strg+A works as well) and use copy and paste.

Unfortunately this only works manually, if you have to export the data very often, the next best step would be to write your own operator for it (which is actually quite simple and requires only basic Java skills): http://docs.rapidminer.com/developers/

David
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0

Yes you can. If you install the Reporting extension from the marketplace (it's free) then you can export the distribution table, plot view or text view.
Here's a sample process.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="7.0.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="reporting:generate_report" compatibility="5.3.000" expanded="true" height="68" name="Generate Report" width="90" x="45" y="34">
        <parameter key="report_name" value="myReport"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="7.0.000" expanded="true" height="68" name="Golf" width="90" x="112" y="85">
        <parameter key="repository_entry" value="//Samples/data/Golf"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="7.0.000" expanded="true" height="68" name="Golf-Testset" width="90" x="179" y="210">
        <parameter key="repository_entry" value="//Samples/data/Golf-Testset"/>
      </operator>
      <operator activated="true" class="naive_bayes" compatibility="7.0.000" expanded="true" height="82" name="Naive Bayes" width="90" x="246" y="34"/>
      <operator activated="true" class="reporting:report" compatibility="5.3.000" expanded="true" height="68" name="Report" width="90" x="380" y="34">
        <parameter key="report_name" value="myReport"/>
        <parameter key="report_item_header" value="Distribution Table"/>
        <parameter key="specified" value="true"/>
        <parameter key="reportable_type" value="Distribution Model"/>
        <parameter key="renderer_name" value="Distribution Table"/>
        <list key="parameters">
          <parameter key="min_row" value="1"/>
          <parameter key="max_row" value="2147483647"/>
          <parameter key="min_column" value="1"/>
          <parameter key="max_column" value="2147483647"/>
          <parameter key="sort_column" value="2147483647"/>
          <parameter key="sort_decreasing" value="false"/>
        </list>
      </operator>
      <operator activated="true" class="apply_model" compatibility="7.0.000" expanded="true" height="82" name="Apply Model" width="90" x="514" y="120">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Golf" from_port="output" to_op="Naive Bayes" to_port="training set"/>
      <connect from_op="Golf-Testset" from_port="output" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Naive Bayes" from_port="model" to_op="Report" to_port="reportable in"/>
      <connect from_op="Report" from_port="reportable out" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="90"/>
      <portSpacing port="sink_result 2" spacing="18"/>
    </process>
  </operator>
</process>