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Can i combine two algorithm for example naive bayes with c.45

Echo1Echo1 Member Posts: 2 Learner I
edited May 2020 in Help
What operator that i need??
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Answers

  • MartinLiebigMartinLiebig Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,533 RM Data Scientist
    Hi
    Stacking or Vote are two operators you may want to use.
    Best,
    Martin
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • Echo1Echo1 Member Posts: 2 Learner I
    Mr mschmitz can you explain the step? Is read excel=>split data=>algorithms=>stack or vote=>apply model=>performance true? Or i miss the step??
  • lionelderkrikorlionelderkrikor RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn
    Hi @Echo1,

    Here an example of process using Vote operator inside a Split Validation operator. (it is the tutorial process of Vote operator)

    Hope this helps,

    Regards,

    Lionel

    <?xml version="1.0" encoding="UTF-8"?><process version="9.5.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.4.000" expanded="true" name="Process" origin="GENERATED_TUTORIAL">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.5.000" expanded="true" height="68" name="Sonar" origin="GENERATED_TUTORIAL" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Sonar"/>
      </operator>
      <operator activated="true" class="split_validation" compatibility="9.5.000" expanded="true" height="124" name="Validation" origin="GENERATED_TUTORIAL" width="90" x="246" y="34">
        <parameter key="create_complete_model" value="false"/>
        <parameter key="split" value="relative"/>
        <parameter key="split_ratio" value="0.7"/>
        <parameter key="training_set_size" value="100"/>
        <parameter key="test_set_size" value="-1"/>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <process expanded="true">
          <operator activated="true" class="vote" compatibility="9.5.000" expanded="true" height="68" name="Vote" origin="GENERATED_TUTORIAL" width="90" x="112" y="34">
            <process expanded="true">
              <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.4.000" expanded="true" height="103" name="Decision Tree" origin="GENERATED_TUTORIAL" width="90" x="313" y="34">
                <parameter key="criterion" value="gain_ratio"/>
                <parameter key="maximal_depth" value="20"/>
                <parameter key="apply_pruning" value="true"/>
                <parameter key="confidence" value="0.25"/>
                <parameter key="apply_prepruning" value="true"/>
                <parameter key="minimal_gain" value="0.1"/>
                <parameter key="minimal_leaf_size" value="2"/>
                <parameter key="minimal_size_for_split" value="4"/>
                <parameter key="number_of_prepruning_alternatives" value="3"/>
              </operator>
              <operator activated="true" class="neural_net" compatibility="9.5.000" expanded="true" height="82" name="Neural Net" origin="GENERATED_TUTORIAL" width="90" x="313" y="187">
                <list key="hidden_layers"/>
                <parameter key="training_cycles" value="500"/>
                <parameter key="learning_rate" value="0.3"/>
                <parameter key="momentum" value="0.2"/>
                <parameter key="decay" value="false"/>
                <parameter key="shuffle" value="true"/>
                <parameter key="normalize" value="true"/>
                <parameter key="error_epsilon" value="1.0E-5"/>
                <parameter key="use_local_random_seed" value="false"/>
                <parameter key="local_random_seed" value="1992"/>
              </operator>
              <operator activated="true" class="support_vector_machine" compatibility="9.5.000" expanded="true" height="124" name="SVM" origin="GENERATED_TUTORIAL" width="90" x="313" y="289">
                <parameter key="kernel_type" value="dot"/>
                <parameter key="kernel_gamma" value="1.0"/>
                <parameter key="kernel_sigma1" value="1.0"/>
                <parameter key="kernel_sigma2" value="0.0"/>
                <parameter key="kernel_sigma3" value="2.0"/>
                <parameter key="kernel_shift" value="1.0"/>
                <parameter key="kernel_degree" value="2.0"/>
                <parameter key="kernel_a" value="1.0"/>
                <parameter key="kernel_b" value="0.0"/>
                <parameter key="kernel_cache" value="200"/>
                <parameter key="C" value="0.0"/>
                <parameter key="convergence_epsilon" value="0.001"/>
                <parameter key="max_iterations" value="100000"/>
                <parameter key="scale" value="true"/>
                <parameter key="calculate_weights" value="true"/>
                <parameter key="return_optimization_performance" value="true"/>
                <parameter key="L_pos" value="1.0"/>
                <parameter key="L_neg" value="1.0"/>
                <parameter key="epsilon" value="0.0"/>
                <parameter key="epsilon_plus" value="0.0"/>
                <parameter key="epsilon_minus" value="0.0"/>
                <parameter key="balance_cost" value="false"/>
                <parameter key="quadratic_loss_pos" value="false"/>
                <parameter key="quadratic_loss_neg" value="false"/>
                <parameter key="estimate_performance" value="false"/>
              </operator>
              <connect from_port="training set 1" to_op="Decision Tree" to_port="training set"/>
              <connect from_port="training set 2" to_op="Neural Net" to_port="training set"/>
              <connect from_port="training set 3" to_op="SVM" to_port="training set"/>
              <connect from_op="Decision Tree" from_port="model" to_port="base model 1"/>
              <connect from_op="Neural Net" from_port="model" to_port="base model 2"/>
              <connect from_op="SVM" from_port="model" to_port="base model 3"/>
              <portSpacing port="source_training set 1" spacing="0"/>
              <portSpacing port="source_training set 2" spacing="72"/>
              <portSpacing port="source_training set 3" spacing="72"/>
              <portSpacing port="source_training set 4" spacing="0"/>
              <portSpacing port="sink_base model 1" spacing="0"/>
              <portSpacing port="sink_base model 2" spacing="72"/>
              <portSpacing port="sink_base model 3" spacing="72"/>
              <portSpacing port="sink_base model 4" spacing="0"/>
            </process>
          </operator>
          <connect from_port="training" to_op="Vote" to_port="training set"/>
          <connect from_op="Vote" 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="9.5.000" expanded="true" height="82" name="Apply Model" origin="GENERATED_TUTORIAL" width="90" x="45" y="34">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance" compatibility="9.5.000" expanded="true" height="82" name="Performance" origin="GENERATED_TUTORIAL" width="90" x="179" y="34">
            <parameter key="use_example_weights" value="true"/>
          </operator>
          <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="Sonar" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 1"/>
      <connect from_op="Validation" from_port="training" to_port="result 3"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="42"/>
      <portSpacing port="sink_result 3" spacing="66"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>


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