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Z-Trasformation

cristianocristiano Member Posts: 16 Contributor II
edited November 2018 in Help
Dear listers,
I've build a model with some Z-transformed variables,

I should pre-transform the variables than apply the model, but the transformation is affected by the mean and the standard deviation of the new dataset values.

So If I apply the model and his rules on the new dataset, the prediction is not always the same, is related to the 'shape' of the new data.

Where I'm wrong?



Thanks for your attention.

Cristiano.

Answers

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Cristiano,
    the Normalization operator will return a preprocessing model. This contains the 'shape' (mean and std. deviation) of the original data and can be applied on new data.

    Simply apply the preprocessing model first, the the prediction model...

    Greetings,
      Sebastian
  • cristianocristiano Member Posts: 16 Contributor II
    WOW

    Thanks for your support!

    So I should use 2 'Apply Model' , the first is for preprocessing and the second for the model,

    In the first node:
    the model input is the proprocessing output of the normalized original data
    data: normalized new data

    In the second node:
    the model input is the original model
    data: labeled data of the first node(normalized new data)


    Is it correct?

    Thanks again for your attention.

    Cristiano.
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Yes, correct :)
  • cristianocristiano Member Posts: 16 Contributor II
    Sebastian Land wrote:

    Yes, correct :)
    Thanks Sebastian,
    when I read the content of preprocessing model I see:

    Normalize 33 attributes to mean 0 and variance 1.
    Using var1 --> mean 0.1

    ... 28 more attributes ..    .

    How I see other normalize parameter's attributes?

    Thanks for your support.

    Cristiano.
  • cristianocristiano Member Posts: 16 Contributor II
    cristiano wrote:

    Thanks Sebastian,
    when I read the content of preprocessing model I see:

    Normalize 33 attributes to mean 0 and variance 1.
    Using var1 --> mean 0.1

    ... 28 more attributes ..    .

    How I see other normalize parameter's attributes?

    Thanks for your support.


    You can write the parameters with 'WriteModel' Operator, easy isn't it? ,)



    Cristiano.

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi,
    which other parameters?

    Greetings,
      Sebastian
  • cristianocristiano Member Posts: 16 Contributor II
    Sebastian Land wrote:

    Hi,
    which other parameters?

    Greetings,
      Sebastian
    Hi,
    Just mean and variance.

    C.
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Ups,
    they aren't shown? Hm. Please make a feature request for that. Normally this should be displayable, since I saw that it is saved in the models.

    Greetings,
      Sebastian
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