Weka Random forest constantly better than Random Forest Rapidminer

Fred12

hi,

I teste W-RAndom Forest and Random Forest from Rapidminer on the same dataset, for W-RF, I got around 89%, whereas for Random Forest I got only 76%, why is that? I thought the Algorithm / Method is the same? Are the implementations so entirely different that I get such a performance discrepancy?

Thomas_Ott

Are you comparing it with the same splitting criteron? This post says that W-RF uses information criteron to split: http://stackoverflow.com/questions/30150970/what-splitting-criterion-does-random-tree-in-weka-3-7-11-use-for-numerical-attri

 

When I do that, the results of the attached Iris data set works the same.

 

<?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="retrieve" compatibility="7.4.000" expanded="true" height="68" name="Retrieve Iris" width="90" x="45" y="187">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="7.4.000" expanded="true" height="103" name="Multiply" width="90" x="179" y="187"/>
      <operator activated="true" class="concurrency:cross_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation (2)" width="90" x="313" y="238">
        <parameter key="sampling_type" value="stratified sampling"/>
        <process expanded="true">
          <operator activated="true" class="weka:W-RandomForest" compatibility="7.3.000" expanded="true" height="82" name="W-RandomForest" width="90" x="112" y="34">
            <parameter key="depth" value="20"/>
          </operator>
          <connect from_port="training set" to_op="W-RandomForest" to_port="training set"/>
          <connect from_op="W-RandomForest" 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"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model (2)" 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 (2)" width="90" x="179" y="34"/>
          <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
          <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
          <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
          <connect from_op="Performance (2)" 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="false" 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>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation" width="90" x="313" y="34">
        <parameter key="sampling_type" value="stratified sampling"/>
        <process expanded="true">
          <operator activated="true" class="concurrency:parallel_random_forest" compatibility="7.4.000" expanded="true" height="82" name="Random Forest" width="90" x="179" y="34">
            <parameter key="criterion" value="information_gain"/>
          </operator>
          <connect from_port="training set" to_op="Random Forest" to_port="training set"/>
          <connect from_op="Random Forest" 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"/>
        </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>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Validation" to_port="example set"/>
      <connect from_op="Multiply" from_port="output 2" to_op="Validation (2)" to_port="example set"/>
      <connect from_op="Validation (2)" from_port="performance 1" to_port="result 2"/>
      <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"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

 

Fred12

that might be the problem, I used gain ratio I will try out information gain

EDIT: with information gain I also got around 77%-... but my dataset is far harder than iris data...

Fred12

is there a solution found to that now?

 

I think it is rather the Random forest implementation from Rapidminer that causes the results rather than any parameter settings...

I mean its a quite big difference, someone should check that...