# Multicriteria Optimization based on different models

Member Posts: 9 Contributor I
edited December 2018 in Help

Hi,

Is it possible to apply three different models with three different target variables (A,B,C) on one dataset simultaneously?

I already built three different models based on the same dataset. One predicts target A, the second target B and the third target C.

My objective is to conduct a multicriteria optimization, so that I receive certain values for A, B and C.

Thank you very much in advance!

• RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,760 Unicorn

If I understand you correctly, you've already trained three different models on the same data set and now you want to take a scoring data set and pass that to the the 3 different models? If yes, then just use a Multiply operator to make 3 copies of the scoring data and then use 3 Apply Model operators to apply your three models.

• Member Posts: 9 Contributor I

Unfortunately, I already tried this solution. The problem is that I need only one result table, meaning one table including the predictions of target A, B and C.

The labels are polynominal - they all contain three categories: small, medium, large. My objective is to receive a prediction for each dataset containing a predicted value for A, B and C.

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

cant you just chain 3x Apply Model with Set Role (to avoid collisions between prediction roles) after another?

Best,

Martin

- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Member Posts: 9 Contributor I

Yes, but then I have three example sets. Is there no opportunity to get only one?

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

i think the answer is a double no.

First of all multiply is not generating a in-memory copy of an example set. We work with a view concept, thus the data will most likely not be copied.

Further you can simply chain the applies. You just need to be sure to change the roles of prediction/confidence because they need to be unique. Have a look at the attached process. The only manual thing is setting the roles correctly. That might be doable with a tiny script if it annoys you too much.

Best,

Martin

`<?xml version="1.0" encoding="UTF-8"?><process version="7.5.003">  <context>    <input/>    <output/>    <macros/>  </context>  <operator activated="true" class="process" compatibility="7.5.003" expanded="true" name="Process">    <process expanded="true">      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="187">        <parameter key="repository_entry" value="//Samples/data/Sonar"/>      </operator>      <operator activated="true" class="multiply" compatibility="7.5.003" expanded="true" height="103" name="Multiply" width="90" x="179" y="187"/>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (2)" width="90" x="380" y="187">        <parameter key="attribute_name" value="attribute_10"/>        <parameter key="target_role" value="label"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.003" expanded="true" height="145" name="Validation (2)" width="90" x="514" y="187">        <parameter key="sampling_type" value="shuffled sampling"/>        <process expanded="true">          <operator activated="true" class="h2o:generalized_linear_model" compatibility="7.5.000" expanded="true" height="103" name="Generalized Linear Model" width="90" x="179" y="34">            <list key="beta_constraints"/>            <list key="expert_parameters"/>          </operator>          <connect from_port="training set" to_op="Generalized Linear Model" to_port="training set"/>          <connect from_op="Generalized Linear Model" 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="7.5.003" 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="7.5.003" 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"/>        </process>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role" width="90" x="380" y="34">        <parameter key="attribute_name" value="attribute_1"/>        <parameter key="target_role" value="label"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.003" expanded="true" height="145" name="Validation" width="90" x="514" y="34">        <parameter key="sampling_type" value="shuffled sampling"/>        <process expanded="true">          <operator activated="true" class="h2o:generalized_linear_model" compatibility="7.5.000" expanded="true" height="103" name="Generalized Linear Model (2)" width="90" x="313" y="34">            <list key="beta_constraints"/>            <list key="expert_parameters"/>          </operator>          <connect from_port="training set" to_op="Generalized Linear Model (2)" to_port="training set"/>          <connect from_op="Generalized Linear Model (2)" from_port="model" to_port="model"/>          <connect from_op="Generalized Linear Model (2)" from_port="weights" to_port="through 1"/>          <portSpacing port="source_training set" spacing="0"/>          <portSpacing port="sink_model" spacing="0"/>          <portSpacing port="sink_through 1" spacing="0"/>          <portSpacing port="sink_through 2" spacing="0"/>        </process>        <process expanded="true">          <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">            <list key="application_parameters"/>          </operator>          <operator activated="true" class="performance" compatibility="7.5.003" 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="source_through 2" spacing="0"/>          <portSpacing port="sink_test set results" spacing="0"/>          <portSpacing port="sink_performance 1" spacing="0"/>          <portSpacing port="sink_performance 2" spacing="0"/>        </process>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Sonar (2)" width="90" x="514" y="391">        <parameter key="repository_entry" value="//Samples/data/Sonar"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (3)" width="90" x="715" y="289">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (3)" width="90" x="849" y="289">        <parameter key="attribute_name" value="prediction(attribute_10)"/>        <parameter key="target_role" value="pra"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (4)" width="90" x="983" y="187">        <list key="application_parameters"/>      </operator>      <connect from_op="Retrieve Sonar" from_port="output" to_op="Multiply" to_port="input"/>      <connect from_op="Multiply" from_port="output 1" to_op="Set Role" to_port="example set input"/>      <connect from_op="Multiply" from_port="output 2" to_op="Set Role (2)" to_port="example set input"/>      <connect from_op="Set Role (2)" from_port="example set output" to_op="Validation (2)" to_port="example set"/>      <connect from_op="Validation (2)" from_port="model" to_op="Apply Model (3)" to_port="model"/>      <connect from_op="Set Role" from_port="example set output" to_op="Validation" to_port="example set"/>      <connect from_op="Validation" from_port="model" to_op="Apply Model (4)" to_port="model"/>      <connect from_op="Retrieve Sonar (2)" from_port="output" to_op="Apply Model (3)" to_port="unlabelled data"/>      <connect from_op="Apply Model (3)" from_port="labelled data" to_op="Set Role (3)" to_port="example set input"/>      <connect from_op="Set Role (3)" from_port="example set output" to_op="Apply Model (4)" to_port="unlabelled data"/>      <connect from_op="Apply Model (4)" from_port="labelled data" 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"/>    </process>  </operator></process>`
- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• RapidMiner Certified Analyst, Member Posts: 344 Unicorn

You have to consider the correlation between the variables, before you can apply this approach. If the features used to predict A are independent from the features to predict B (and so forth with C, D, etc.), then it is safe to apply separate models for each of them. If that's not the case, you will have biased predictions.

Take a look at this Stack Exchange post:

https://stats.stackexchange.com/questions/18151/methods-to-predict-multiple-dependent-variables

EDIT: Actually you can apply the different models and then look for correlations between the residuals. If you observe no correlation, then the approach is correct.

Best,

Sebastian

• Member Posts: 9 Contributor I

Thank you! I think I understood what you suggested.

However, I am still struggling with the solution. I tried to import your process but it does not work with my version (7.2.002).

With the three example sets, I meant that I received three data sets with predicted values- so this had nothing to do with the "multiply" operator.

Furthermore, I want to apply my already built models, therefore I have three operators named "retrieve model". When I chain the "apply model" operators including the "set role" operators, I only receive the example set with the last label. This is not surprising as the data set to be included into the "apply model" operator is usually unlabeled.

Do you have another idea how to solve this problem?

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

any chance you can post a dummy process (or two)?

Best,

Martin

- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Member Posts: 9 Contributor I

Does this one help?

`<?xml version="1.0" encoding="UTF-8"?><process version="7.2.002">  <context>    <input/>    <output/>    <macros/>  </context>  <operator activated="true" class="process" compatibility="7.2.002" expanded="true" name="Process">    <process expanded="true">      <operator activated="true" class="retrieve" compatibility="7.2.002" expanded="true" height="68" name="Retrieve dataset xy" width="90" x="45" y="85">        <parameter key="repository_entry" value="//dataset xy"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.2.002" expanded="true" height="68" name="Retrieve Model_small" width="90" x="246" y="340">        <parameter key="repository_entry" value="//Model_small"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.2.002" expanded="true" height="68" name="Retrieve Model_large" width="90" x="45" y="187">        <parameter key="repository_entry" value="//Model_large"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.2.002" expanded="true" height="68" name="Retrieve Model_medium" width="90" x="112" y="289">        <parameter key="repository_entry" value="//Model_Medium"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.2.002" expanded="true" height="82" name="Select Attributes" width="90" x="112" y="34">        <parameter key="attribute_filter_type" value="subset"/>        <parameter key="attributes" value="........."/>      </operator>      <operator activated="true" class="set_role" compatibility="7.2.002" expanded="true" height="82" name="Set Role" width="90" x="246" y="34">        <parameter key="attribute_name" value="Class_Large"/>        <parameter key="target_role" value="label"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.2.002" expanded="true" height="82" name="Apply Model (2)" width="90" x="380" y="34">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.2.002" expanded="true" height="82" name="Select Attributes (2)" width="90" x="447" y="34">        <parameter key="include_special_attributes" value="true"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.2.002" expanded="true" height="82" name="Set Role (2)" width="90" x="581" y="34">        <parameter key="attribute_name" value="Class_Medium"/>        <parameter key="target_role" value="label"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.2.002" expanded="true" height="82" name="Apply Model" width="90" x="313" y="187">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.2.002" expanded="true" height="82" name="Set Role (3)" width="90" x="447" y="187">        <parameter key="attribute_name" value="Class_Small"/>        <parameter key="target_role" value="label"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.2.002" expanded="true" height="82" name="Apply Model (3)" width="90" x="715" y="136">        <list key="application_parameters"/>      </operator>      <connect from_op="Retrieve dataset xy" from_port="output" to_op="Select Attributes" to_port="example set input"/>      <connect from_op="Retrieve Model_small" from_port="output" to_op="Apply Model (3)" to_port="model"/>      <connect from_op="Retrieve Model_large" from_port="output" to_op="Apply Model (2)" to_port="model"/>      <connect from_op="Retrieve Model_medium" from_port="output" to_op="Apply Model" to_port="model"/>      <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>      <connect from_op="Set Role" from_port="example set output" to_op="Apply Model (2)" to_port="unlabelled data"/>      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Select Attributes (2)" to_port="example set input"/>      <connect from_op="Select Attributes (2)" from_port="original" to_op="Set Role (2)" to_port="example set input"/>      <connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>      <connect from_op="Apply Model" from_port="labelled data" to_op="Set Role (3)" to_port="example set input"/>      <connect from_op="Set Role (3)" from_port="example set output" to_op="Apply Model (3)" to_port="unlabelled data"/>      <connect from_op="Apply Model (3)" from_port="labelled data" 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"/>    </process>  </operator></process>`
• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

`<?xml version="1.0" encoding="UTF-8"?><process version="7.5.003">  <context>    <input/>    <output/>    <macros/>  </context>  <operator activated="true" class="process" compatibility="7.5.003" expanded="true" name="Process">    <process expanded="true">      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve dataset xy" width="90" x="45" y="34">        <parameter key="repository_entry" value="//dataset xy"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_small" width="90" x="246" y="340">        <parameter key="repository_entry" value="//Model_small"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_large" width="90" x="45" y="187">        <parameter key="repository_entry" value="//Model_large"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_medium" width="90" x="112" y="289">        <parameter key="repository_entry" value="//Model_Medium"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.5.003" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">        <parameter key="attribute_filter_type" value="subset"/>        <parameter key="attributes" value="........."/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">        <parameter key="attribute_name" value="prediction(Class_Large)"/>        <parameter key="target_role" value="pr1"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (2)" width="90" x="447" y="34">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.5.003" expanded="true" height="82" name="Select Attributes (2)" width="90" x="581" y="34">        <parameter key="include_special_attributes" value="true"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (2)" width="90" x="715" y="34">        <parameter key="attribute_name" value="prediction(Class_Medium)"/>        <parameter key="target_role" value="pr2"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model" width="90" x="715" y="187">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (3)" width="90" x="648" y="442">        <parameter key="attribute_name" value="prediction(Class_Small)"/>        <parameter key="target_role" value="pr3"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (3)" width="90" x="849" y="238">        <list key="application_parameters"/>      </operator>      <connect from_op="Retrieve dataset xy" from_port="output" to_op="Select Attributes" to_port="example set input"/>      <connect from_op="Retrieve Model_small" from_port="output" to_op="Apply Model (3)" to_port="model"/>      <connect from_op="Retrieve Model_large" from_port="output" to_op="Apply Model (2)" to_port="model"/>      <connect from_op="Retrieve Model_medium" from_port="output" to_op="Apply Model" to_port="model"/>      <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>      <connect from_op="Set Role" from_port="example set output" to_op="Apply Model (2)" to_port="unlabelled data"/>      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Select Attributes (2)" to_port="example set input"/>      <connect from_op="Select Attributes (2)" from_port="original" to_op="Set Role (2)" to_port="example set input"/>      <connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>      <connect from_op="Apply Model" from_port="labelled data" to_op="Set Role (3)" to_port="example set input"/>      <connect from_op="Set Role (3)" from_port="example set output" to_op="Apply Model (3)" to_port="unlabelled data"/>      <connect from_op="Apply Model (3)" from_port="labelled data" 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"/>    </process>  </operator></process>`
- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Member Posts: 9 Contributor I

Good morning,

Thank you very much.

I just tried to change my xml code according to your suggestions. As it did not work at the first try, I tried to integrate variables called "pediction (Class_Small)".... into my dataset.

However, it still does not give out the desired results.

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

What is the remaining issue?

Best,

Martin

- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Member Posts: 9 Contributor I

I only receive values for the last predicted variable ("prediction(class_large)"), "prediction(class_medium)" is empty and "prediction(class_small)" does not exist in the final example set.

• Member Posts: 9 Contributor I

I tried to conduct a data join with the three result example sets. For testing this, I tried to join only two (including predictions of "class_small" and "class_medium), but then I receive a warning that the attribute "class_small" has two different roles in the input sets (regular vs. label). Do you know how to solve this problem?

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

i think you need to rename the attributes as well, see attached process.

Why are you doing this by the way? I have the odd feeling that Polynominal by Binominal Classification is the nice solution of your problem.

Best,

Martin

`<?xml version="1.0" encoding="UTF-8"?><process version="7.5.003">  <context>    <input/>    <output/>    <macros/>  </context>  <operator activated="true" class="process" compatibility="7.5.003" expanded="true" name="Process">    <process expanded="true">      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve dataset xy" width="90" x="45" y="34">        <parameter key="repository_entry" value="//dataset xy"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_small" width="90" x="179" y="544">        <parameter key="repository_entry" value="//Model_small"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_large" width="90" x="45" y="340">        <parameter key="repository_entry" value="//Model_large"/>      </operator>      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Model_medium" width="90" x="45" y="442">        <parameter key="repository_entry" value="//Model_Medium"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.5.003" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">        <parameter key="attribute_filter_type" value="subset"/>        <parameter key="attributes" value="........."/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">        <parameter key="attribute_name" value="prediction(Class_Large)"/>        <parameter key="target_role" value="pr1"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (2)" width="90" x="447" y="136">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="select_attributes" compatibility="7.5.003" expanded="true" height="82" name="Select Attributes (2)" width="90" x="581" y="34">        <parameter key="include_special_attributes" value="true"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (2)" width="90" x="715" y="34">        <parameter key="attribute_name" value="prediction(Class_Medium)"/>        <parameter key="target_role" value="pr2"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="rename_by_replacing" compatibility="7.5.003" expanded="true" height="82" name="Rename by Replacing" width="90" x="782" y="136">        <parameter key="include_special_attributes" value="true"/>        <parameter key="replace_what" value="prediction\((.*)\)"/>        <parameter key="replace_by" value="\$1_FirstModel"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model" width="90" x="782" y="238">        <list key="application_parameters"/>      </operator>      <operator activated="true" class="set_role" compatibility="7.5.003" expanded="true" height="82" name="Set Role (3)" width="90" x="916" y="289">        <parameter key="attribute_name" value="prediction(Class_Small)"/>        <parameter key="target_role" value="pr3"/>        <list key="set_additional_roles"/>      </operator>      <operator activated="true" class="rename_by_replacing" compatibility="7.5.003" expanded="true" height="82" name="Rename by Replacing (2)" width="90" x="983" y="442">        <parameter key="include_special_attributes" value="true"/>        <parameter key="replace_what" value="prediction\((.*)\)"/>        <parameter key="replace_by" value="\$1_SecondModel"/>      </operator>      <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (3)" width="90" x="1050" y="544">        <list key="application_parameters"/>      </operator>      <connect from_op="Retrieve dataset xy" from_port="output" to_op="Select Attributes" to_port="example set input"/>      <connect from_op="Retrieve Model_small" from_port="output" to_op="Apply Model (3)" to_port="model"/>      <connect from_op="Retrieve Model_large" from_port="output" to_op="Apply Model (2)" to_port="model"/>      <connect from_op="Retrieve Model_medium" from_port="output" to_op="Apply Model" to_port="model"/>      <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>      <connect from_op="Set Role" from_port="example set output" to_op="Apply Model (2)" to_port="unlabelled data"/>      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Select Attributes (2)" to_port="example set input"/>      <connect from_op="Select Attributes (2)" from_port="original" to_op="Set Role (2)" to_port="example set input"/>      <connect from_op="Set Role (2)" from_port="example set output" to_op="Rename by Replacing" to_port="example set input"/>      <connect from_op="Rename by Replacing" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>      <connect from_op="Apply Model" from_port="labelled data" to_op="Set Role (3)" to_port="example set input"/>      <connect from_op="Set Role (3)" from_port="example set output" to_op="Rename by Replacing (2)" to_port="example set input"/>      <connect from_op="Rename by Replacing (2)" from_port="example set output" to_op="Apply Model (3)" to_port="unlabelled data"/>      <connect from_op="Apply Model (3)" from_port="labelled data" 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"/>    </process>  </operator></process>`

- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Member Posts: 9 Contributor I

Actually, I want to determine certain attribute ranges of my input variables, which should lead to distinct values in my target variables.

As an example:

Attributes "weight"= 1000-2000g, "material"= Cu or Ni and "size"= 10-20mm lead to targets "class_small"=low, "class_medium"=low and "class_large"=high.

Does this somehow work in RapidMiner?

• RapidMiner Certified Analyst, Member Posts: 344 Unicorn

*Everything* can be done with Rapidminer!

I think in this case the "Discretize" set of operators can come in handy. The problem will be much easier dealing with cathegorical variables. I would try training decision trees for each variable.

• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,089 RM Data Scientist

Hi,

have you considered to take this as an Association RUle problem instead of a supervised learning one?

Are you maybe attending IDS - we might catch a bit of time there. Details: http://ids2017.rapidminer.com/

Best,

Martin

- Head of Data Science Services at RapidMiner -
Dortmund, Germany