Building models - but same procedures of different results

Hi, gurus!

Nice day! A newbie here! :)

I studied ODM for a month now. I'm currently building a model based on the instructions that I got from a colleague.
Function Type: Classification
Algorithm: Support vector Machine
-> table
-> same unique key
-> the same target and the input column
-> same target value
-> the same settings: disabled sample test, build 70% - 30% table test, everything else default table.
-> everything else, default

I have already created 6 models using exactly the same steps as above. However, I was a different test measure (predictive confidence, precision, etc.). Is this normal? Or am I just missed a few important stages/settings?

Your "expert comments" are quite welcome. Thank you very much! =)

Georgette

Hello
N ° in fact you use 'Split' of 70/30 for Build/Test and there is a randomization it. For not to bias certain elements of data mining tools in phase of construction (such as ODM) usually before full data shuffle of splints for build and test games. In order to solve this problem you must create tables in the ODM by dividing all of the data by yourself. Then in the phase of construction fund split box then train your model using 100% of all generation (table), that you have prepared.

Kind regards
Hüsnü Sensoy

Tags: Business Intelligence

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    ssbsect_crn,
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    ssbsect_crn,
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    ssbsect_crn,
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    ssbsect_term_code,
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    Thank you

    user3367455 wrote:
    Hello
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    Hello

    Different how?

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    Concerning
    Peter

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