Statistic Book to understand rapidminer

VektorVektor Member Posts: 3 Contributor I
edited November 2018 in Help
Hi

I have basic knowledge in statistic. I'd like to understand rapidminer, how it works and especially how such a tree is build. Is there a book or a download in the internet, which can help to understand the architecure of rapidminer. What are you suggesting?

my data is on excel, i'd need it for time series, optimization, prognoses etc.

Thanks a lot for your help.

Kind regards,

Vektor

Answers

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,525   Unicorn
    Hi,
    we are currently working on a rapid miner book which will answer all your questions. But unfortunately it will take some time until we are finished with writing...
    Until then there's no special rapid miner book, as far as I know. To get answers on you questions, you might visit one of our course, either live or via web.

    Greetings,
      Sebastian
  • VektorVektor Member Posts: 3 Contributor I
    Looking forward to your book  :)

    Are there any other possibilities like student tutarials available ? Please help me.

    I'd like to get into the software a bit before booking a course.

    Any tutorials?

    Many Thanks

    Vektor
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,525   Unicorn
    Hi,
    together with the program comes a couple of example processes. They are a good starting point, since they demonstrate the general patterns of process construction. All important operators are covered and described. A more detailed description of each operator is available in the operator description within the program. You might reach it by pressing F1 or over the context menu.

    Beside of this, we will soon have a collection of introduction videos which will be much more pricey than a course or webinar.

    If your are especially interested in decision trees, you might take a look at Quinlan's C4.5 book, where he published the algorithm for this type of decision tree.

    Greetings,
      Sebastian
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