Interpreting 'Weight By' Operator Results

B00100719B00100719 Member Posts: 11 Contributor II
Hi - Simple question.  When interpreting 'Weight By' operator results, e.g Weight by SVM - is it the absolute value of the weight (i.e. ignoring the minus in negative weights) that should be considered or does, say, and attribute with a weight of -0.47 really have less value than one with a value of 0.31.  In short, should I be ignoring the minus sign?


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    varunm1varunm1 Moderator, Member Posts: 1,207 Unicorn
    edited January 2019
    Hi  @B00100719

    Can you take a look at below thread where there is a similar discussion about negative weights. I see that they both (+ and -) has their own significance based on the .


    @lionelderkrikor can pitch in for this question I guess :smile:


    Be Safe. Follow precautions and Maintain Social Distancing

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    B00100719B00100719 Member Posts: 11 Contributor II
    Thank you - That thread seems to suggest the interpretation depends on the process and dataset.  I don't really understand.  Mine is a binomial classification problem and I would have thought the interpretation would be the same regardless. I'm still a little confused as to should I regard the heavily negative weighted attributes as significant or not!
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    B00100719B00100719 Member Posts: 11 Contributor II
    And another thing I don't understand.  How come when I run 'Weight by Rule' with the "Normalize Weights" checkbox ticked, I get results set that is in a completely different order to when I run it without that checkbox ticked?
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    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,517 RM Data Scientist
    if you tick normalize, then the biggest weight is set to 1. You basically devide all weights by the maximum of all your weights. This can be useful for some filters if your weights are unnormalized by definition (like Chi-Square values)

    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
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    B00100719B00100719 Member Posts: 11 Contributor II
    @mschmitz - My question was two fold.  1.  Should you take the absolute value?  2. Why has normalizing resulted in a different order of importance
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    Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn
    Yes, my understanding is that you should consider the absolute value of the weights to understand strength of effect.
    I will defer to @mschmitz regarding the effect of the normalization option.  I agree that I would not expect it to change the order of the weights, unless it is doing the normalizing on the underlying attributes first and then the weighting, in which case this is feasible.
    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
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