Generalized Linear Model (GLM)

Hi,

Did you finally receive any content or example of GLM functionality in Rapidminer?

I am looking for the same.

Thanks

A

it's in for quite a while now. just search for glm in the operators.

Best,

Martin

Yes, I read through the operators documentations. I would like more info on how to specify beta constraints.

My model has around 30 input variables and I want to constrain the coefficients of few variables as positive (because I know that the relationship is +ve) by specifying a lower bound as 0 and upper bound as +infinity. I am struggling to implement it in the paramters window (screenshots):

- What is the 'category' input right next to attribute name?

- How to input +infinity as upper bound?

Thanks

A

screenshot of the documentation

Hi @am_das,

That is a good question. The beta constraint parameter can be setup in your GLM.

In my attached process, I used deals data with customer profile. Input data has a categorical variable "payement method" and suppose I know the coefficients (beta) for that "credit card" category need to be positve, then I set up the constraints for the coefficients of that category. 

upper_bounds is (optional): The upper bounds of the beta. Must be greater than or equal to lower_bounds. You need to have real value there.

Hope this helps.

<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.1.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="8.1.001" expanded="true" height="68" name="Retrieve Deals" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Deals"/>
      </operator>
      <operator activated="true" class="h2o:generalized_linear_model" compatibility="7.2.000" expanded="true" height="124" name="Generalized Linear Model" width="90" x="179" y="34">
        <parameter key="specify_beta_constraints" value="true"/>
        <list key="beta_constraints">
          <parameter key="Payment Method.credit card" value="0\.01.5\.0.0\.0.0\.0"/>
        </list>
        <list key="expert_parameters"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="8.1.001" expanded="true" height="68" name="Retrieve Deals-Testset" width="90" x="179" y="238">
        <parameter key="repository_entry" value="//Samples/data/Deals-Testset"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="7.1.001" expanded="true" height="82" name="Apply Model" width="90" x="380" y="34">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance_classification" compatibility="8.1.001" expanded="true" height="82" name="Performance" width="90" x="514" y="85">
        <list key="class_weights"/>
      </operator>
      <connect from_op="Retrieve Deals" from_port="output" to_op="Generalized Linear Model" to_port="training set"/>
      <connect from_op="Generalized Linear Model" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Retrieve Deals-Testset" from_port="output" 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="Apply Model" from_port="model" to_port="result 2"/>
      <connect from_op="Performance" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance" from_port="example set" to_port="result 3"/>
      <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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

YY