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Validation Performance Issue

AtiahKhoirunnisaAtiahKhoirunnisa Member Posts: 5 Contributor II
edited September 2019 in Help

Hi everyone,

I have a question, when i apply both cross validation and split validation at one time using multiply, the performance results of either cross validation operator or split validation operator have difference accuracy with when i only apply one of cross validation or split validation separately ( i mean i enable one of them ), why ? I provide both two scripts one for when apply both and one of only cross validation

*** This one script for process when apply both cross val and split val
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="retrieve" compatibility="9.3.001" expanded="true" height="68" name="Retrieve Customer Data" origin="GENERATED_SAMPLE" width="90" x="45" y="136">
    <parameter key="repository_entry" value="//Samples/Templates/Churn Modeling/Customer Data"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="set_role" compatibility="9.3.001" expanded="true" height="82" name="Set Role" origin="GENERATED_SAMPLE" width="90" x="179" y="85">
    <parameter key="attribute_name" value="ChurnIndicator"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="numerical_to_binominal" compatibility="9.3.001" expanded="true" height="82" name="Numerical to Binominal" origin="GENERATED_SAMPLE" width="90" x="313" y="85">
    <parameter key="attribute_filter_type" value="single"/>
    <parameter key="attribute" value="ChurnIndicator"/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="numeric"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="real"/>
    <parameter key="block_type" value="value_series"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_series_end"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="true"/>
    <parameter key="min" value="0.0"/>
    <parameter key="max" value="0.5"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="concurrency:cross_validation" compatibility="8.2.000" expanded="true" height="145" name="Cross Validation" origin="GENERATED_SAMPLE" width="90" x="514" y="34">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="true"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
      <operator activated="true" class="sample" compatibility="9.3.001" expanded="true" height="82" name="Sample" origin="GENERATED_SAMPLE" width="90" x="45" y="34">
        <parameter key="sample" value="relative"/>
        <parameter key="balance_data" value="true"/>
        <parameter key="sample_size" value="100"/>
        <parameter key="sample_ratio" value="0.1"/>
        <parameter key="sample_probability" value="0.1"/>
        <list key="sample_size_per_class"/>
        <list key="sample_ratio_per_class">
          <parameter key="true" value="1.0"/>
          <parameter key="false" value="0.02"/>
        </list>
        <list key="sample_probability_per_class">
          <parameter key="false" value="0.02"/>
          <parameter key="true" value="1.0"/>
        </list>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.3.001" expanded="true" height="82" name="Decision Tree" origin="GENERATED_SAMPLE" 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>
      <connect from_port="training set" to_op="Sample" to_port="example set input"/>
      <connect from_op="Sample" from_port="example set output" to_op="Decision Tree" to_port="training set"/>
      <connect from_op="Decision Tree" 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"/>
      <description align="left" color="yellow" colored="false" height="393" resized="false" width="217" x="10" y="10">&lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; Many more customers stay than churn (hopefully!). In order for our model to learn how churners behave, we re-balance the data to focus on the case we're interested in. This is like a magnifying glass on churn!&lt;br&gt;&lt;br&gt;Take a look at the 'Sample' operator.</description>
      <description align="left" color="green" colored="true" height="395" resized="false" width="234" x="242" y="10">&lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; Let's now add a model trainer, like a Decision Tree.&lt;br&gt;&lt;br&gt;Try different values for the parameters, in particular, the 'minimal gain'. The 'Wisdom of the Crowds' recommendation helps you find reasonable values.</description>
    </process>
    <process expanded="true">
      <operator activated="true" class="apply_model" compatibility="9.3.001" expanded="true" height="82" name="Apply Model" origin="GENERATED_SAMPLE" width="90" x="112" y="34">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_binominal_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance (Binominal Classification)" origin="GENERATED_SAMPLE" width="90" x="246" y="34">
        <parameter key="manually_set_positive_class" value="false"/>
        <parameter key="main_criterion" value="first"/>
        <parameter key="accuracy" value="true"/>
        <parameter key="classification_error" value="false"/>
        <parameter key="kappa" value="false"/>
        <parameter key="AUC (optimistic)" value="false"/>
        <parameter key="AUC" value="false"/>
        <parameter key="AUC (pessimistic)" value="false"/>
        <parameter key="precision" value="false"/>
        <parameter key="recall" value="false"/>
        <parameter key="lift" value="false"/>
        <parameter key="fallout" value="false"/>
        <parameter key="f_measure" value="false"/>
        <parameter key="false_positive" value="false"/>
        <parameter key="false_negative" value="false"/>
        <parameter key="true_positive" value="false"/>
        <parameter key="true_negative" value="false"/>
        <parameter key="sensitivity" value="false"/>
        <parameter key="specificity" value="false"/>
        <parameter key="youden" value="false"/>
        <parameter key="positive_predictive_value" value="false"/>
        <parameter key="negative_predictive_value" value="false"/>
        <parameter key="psep" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <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 (Binominal Classification)" to_port="labelled data"/>
      <connect from_op="Performance (Binominal Classification)" from_port="performance" to_port="performance 1"/>
      <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="red" colored="true" height="390" resized="false" width="259" x="92" y="10">&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;The model trained on the training data is applied to the independent test data set and the model performance is calculated.&lt;br&gt;&lt;br&gt;The performance values obtained on the different folds of the cross-validation are finally averaged to produce an average performance measure as well as a measure of its dispersion - which gives an estimate of the model stability when applied to different data samples.</description>
    </process>
  </operator>
</process>
</code><?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="retrieve" compatibility="9.3.001" expanded="true" height="68" name="Retrieve Customer Data" origin="GENERATED_SAMPLE" width="90" x="45" y="136">
    <parameter key="repository_entry" value="//Samples/Templates/Churn Modeling/Customer Data"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="set_role" compatibility="9.3.001" expanded="true" height="82" name="Set Role" origin="GENERATED_SAMPLE" width="90" x="179" y="85">
    <parameter key="attribute_name" value="ChurnIndicator"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="numerical_to_binominal" compatibility="9.3.001" expanded="true" height="82" name="Numerical to Binominal" origin="GENERATED_SAMPLE" width="90" x="313" y="85">
    <parameter key="attribute_filter_type" value="single"/>
    <parameter key="attribute" value="ChurnIndicator"/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="numeric"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="real"/>
    <parameter key="block_type" value="value_series"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_series_end"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="true"/>
    <parameter key="min" value="0.0"/>
    <parameter key="max" value="0.5"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="multiply" compatibility="9.3.001" expanded="true" height="103" name="Multiply" width="90" x="380" y="187"/>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="split_validation" compatibility="9.3.001" expanded="true" height="124" name="Validation" width="90" x="514" y="289">
    <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="true"/>
    <parameter key="local_random_seed" value="1992"/>
    <process expanded="true">
      <operator activated="true" class="sample" compatibility="9.3.001" expanded="true" height="82" name="Sample (2)" origin="GENERATED_SAMPLE" width="90" x="45" y="34">
        <parameter key="sample" value="relative"/>
        <parameter key="balance_data" value="true"/>
        <parameter key="sample_size" value="100"/>
        <parameter key="sample_ratio" value="0.1"/>
        <parameter key="sample_probability" value="0.1"/>
        <list key="sample_size_per_class"/>
        <list key="sample_ratio_per_class">
          <parameter key="true" value="1.0"/>
          <parameter key="false" value="0.02"/>
        </list>
        <list key="sample_probability_per_class">
          <parameter key="false" value="0.02"/>
          <parameter key="true" value="1.0"/>
        </list>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.3.001" expanded="true" height="103" name="Decision Tree (2)" origin="GENERATED_SAMPLE" 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>
      <connect from_port="training" to_op="Sample (2)" to_port="example set input"/>
      <connect from_op="Sample (2)" from_port="example set output" to_op="Decision Tree (2)" to_port="training set"/>
      <connect from_op="Decision Tree (2)" 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="9.3.001" expanded="true" height="82" name="Apply Model (2)" origin="GENERATED_SAMPLE" width="90" x="112" y="34">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_binominal_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance (Binominal Classification) (2)" origin="GENERATED_SAMPLE" width="90" x="246" y="34">
        <parameter key="manually_set_positive_class" value="false"/>
        <parameter key="main_criterion" value="first"/>
        <parameter key="accuracy" value="true"/>
        <parameter key="classification_error" value="false"/>
        <parameter key="kappa" value="false"/>
        <parameter key="AUC (optimistic)" value="false"/>
        <parameter key="AUC" value="false"/>
        <parameter key="AUC (pessimistic)" value="false"/>
        <parameter key="precision" value="false"/>
        <parameter key="recall" value="false"/>
        <parameter key="lift" value="false"/>
        <parameter key="fallout" value="false"/>
        <parameter key="f_measure" value="false"/>
        <parameter key="false_positive" value="false"/>
        <parameter key="false_negative" value="false"/>
        <parameter key="true_positive" value="false"/>
        <parameter key="true_negative" value="false"/>
        <parameter key="sensitivity" value="false"/>
        <parameter key="specificity" value="false"/>
        <parameter key="youden" value="false"/>
        <parameter key="positive_predictive_value" value="false"/>
        <parameter key="negative_predictive_value" value="false"/>
        <parameter key="psep" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <parameter key="use_example_weights" value="true"/>
      </operator>
      <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 (Binominal Classification) (2)" to_port="labelled data"/>
      <connect from_op="Performance (Binominal Classification) (2)" 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>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <operator activated="true" class="concurrency:cross_validation" compatibility="8.2.000" expanded="true" height="145" name="Cross Validation" origin="GENERATED_SAMPLE" width="90" x="514" y="34">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="true"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
      <operator activated="true" class="sample" compatibility="9.3.001" expanded="true" height="82" name="Sample" origin="GENERATED_SAMPLE" width="90" x="45" y="34">
        <parameter key="sample" value="relative"/>
        <parameter key="balance_data" value="true"/>
        <parameter key="sample_size" value="100"/>
        <parameter key="sample_ratio" value="0.1"/>
        <parameter key="sample_probability" value="0.1"/>
        <list key="sample_size_per_class"/>
        <list key="sample_ratio_per_class">
          <parameter key="true" value="1.0"/>
          <parameter key="false" value="0.02"/>
        </list>
        <list key="sample_probability_per_class">
          <parameter key="false" value="0.02"/>
          <parameter key="true" value="1.0"/>
        </list>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.3.001" expanded="true" height="82" name="Decision Tree" origin="GENERATED_SAMPLE" 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>
      <connect from_port="training set" to_op="Sample" to_port="example set input"/>
      <connect from_op="Sample" from_port="example set output" to_op="Decision Tree" to_port="training set"/>
      <connect from_op="Decision Tree" 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"/>
      <description align="left" color="yellow" colored="false" height="393" resized="false" width="217" x="10" y="10">&lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; Many more customers stay than churn (hopefully!). In order for our model to learn how churners behave, we re-balance the data to focus on the case we're interested in. This is like a magnifying glass on churn!&lt;br&gt;&lt;br&gt;Take a look at the 'Sample' operator.</description>
      <description align="left" color="green" colored="true" height="395" resized="false" width="234" x="242" y="10">&lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; &lt;br&gt; Let's now add a model trainer, like a Decision Tree.&lt;br&gt;&lt;br&gt;Try different values for the parameters, in particular, the 'minimal gain'. The 'Wisdom of the Crowds' recommendation helps you find reasonable values.</description>
    </process>
    <process expanded="true">
      <operator activated="true" class="apply_model" compatibility="9.3.001" expanded="true" height="82" name="Apply Model" origin="GENERATED_SAMPLE" width="90" x="112" y="34">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_binominal_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance (Binominal Classification)" origin="GENERATED_SAMPLE" width="90" x="246" y="34">
        <parameter key="manually_set_positive_class" value="false"/>
        <parameter key="main_criterion" value="first"/>
        <parameter key="accuracy" value="true"/>
        <parameter key="classification_error" value="false"/>
        <parameter key="kappa" value="false"/>
        <parameter key="AUC (optimistic)" value="false"/>
        <parameter key="AUC" value="false"/>
        <parameter key="AUC (pessimistic)" value="false"/>
        <parameter key="precision" value="false"/>
        <parameter key="recall" value="false"/>
        <parameter key="lift" value="false"/>
        <parameter key="fallout" value="false"/>
        <parameter key="f_measure" value="false"/>
        <parameter key="false_positive" value="false"/>
        <parameter key="false_negative" value="false"/>
        <parameter key="true_positive" value="false"/>
        <parameter key="true_negative" value="false"/>
        <parameter key="sensitivity" value="false"/>
        <parameter key="specificity" value="false"/>
        <parameter key="youden" value="false"/>
        <parameter key="positive_predictive_value" value="false"/>
        <parameter key="negative_predictive_value" value="false"/>
        <parameter key="psep" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <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 (Binominal Classification)" to_port="labelled data"/>
      <connect from_op="Performance (Binominal Classification)" from_port="performance" to_port="performance 1"/>
      <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="red" colored="true" height="390" resized="false" width="259" x="92" y="10">&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;The model trained on the training data is applied to the independent test data set and the model performance is calculated.&lt;br&gt;&lt;br&gt;The performance values obtained on the different folds of the cross-validation are finally averaged to produce an average performance measure as well as a measure of its dispersion - which gives an estimate of the model stability when applied to different data samples.</description>
    </process>
  </operator>
</process>
</pre><div>****This script below is for only cross validation</div><pre class="CodeBlock"><code>

Thank you
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Best Answer

Answers

  • Options
    varunm1varunm1 Moderator, Member Posts: 1,207 Unicorn
    Hello @Atiah

    Did you selected set local random seed in cross validation and split validation operators? If you do this, your results doesn't change.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • Options
    varunm1varunm1 Moderator, Member Posts: 1,207 Unicorn
    edited September 2019
    @sgenzer or @Tghadially can you help in checking the process in rapidminer by copying XML code from here? I am having an issue from yesterday, not sure why the copied code is not producing a process in RM.

    Thanks
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • Options
    lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn
    Hi @varunm1,

    After checking the XMLs, the XMLs seems to be invalid because there are multiple lines with : 
    <?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
     : 



    @Atiah Can you export your processes in .rmp files (via File -> Export Process)

    Thanks,

    Regards,

    Lionel

  • Options
    varunm1varunm1 Moderator, Member Posts: 1,207 Unicorn
    @lionelderkrikor, this is the same case with others as well, yesterday I used XML from notepad still it had a similar pattern not sure why. The notepad I am referring is in this thread and I asked this user to provide .rmp.
    https://community.rapidminer.com/discussion/56127/testing-of-automodel#latest
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

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    sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Manager
    hi all - yes I know this XML import/export thing is still very clunky. I apologize for this. The good news is that I am actually working now (i.e. this week!) on a new solution to this. It is a huge pain point for all.

    For now I recommend you attach the .rmp file to a discussion rather than cut-and-paste. It will import much better.

    Scott

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    AtiahKhoirunnisaAtiahKhoirunnisa Member Posts: 5 Contributor II
    Hello @varunm1 thank you for your help, yes it works, but can i have explanation why did
    it work like that, and what is local random seed exactly? 
    Thank you
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    AtiahKhoirunnisaAtiahKhoirunnisa Member Posts: 5 Contributor II
    Hello @varunm1 Thank you for your explanation, it is really helpful 
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