About the LinearRegression operator

Legacy UserLegacy User Member Posts: 0 Newbie
edited November 2018 in Help

I am a new user of rapidminer. I tried to use the LinearRegression operator and I ran with my dataset. But in the end I did not get an equation. I am currently trying to train a model.

My process are as follows:


 >CSV Example Source

 >Attribute Filter


 >Genetic Algorithm

   >>Operator Chain







 >CSV Example Set Writer



  • haddockhaddock Member Posts: 849 Maven
    Hi there,

    The optimisation produces a parameter set for the learning operator and anything else you tweak, rather than a model. To keep it simple the process is like this.

    1. Generate a parameter set - you've done that.

    2. Apply that set using the parameter setter operator. You'll need to map between the optimised parameters and the ones you will use. So if I had optimised an SVM and some Validation settings I would have this sort of mapping.

    <operator name="ParameterSetter" class="ParameterSetter" activated="no">
                <list key="name_map">
                  <parameter key="NNValidation" value="NNValidation (2)"/>
                  <parameter key="LIbSVMLearner" value="LibSVMLearner (2)"/>

    3. Run the same learner on an appropriate dataset ( careful here ). That will produce the optimised model which has this sort of form.

    31.736 * a1 + 42.948 * a2 + 23.773 * a3 + 3.706 * a4 - 4.184 * a5 - 304.228

    Where a1 etc. are the attribute names.

    With RM a big skill is to keep track of the inputs and ouputs of the operators, because sometimes it is not what you think!

  • Legacy UserLegacy User Member Posts: 0 Newbie
    Hi haddock,

    Thanks for your suggestion.

    So the parameter setter operator should include two learner operators and placed after genetic algorithm?

  • haddockhaddock Member Posts: 849 Maven
    No, just the one learner after setting the parameters.
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