The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
Operators to split data into multiple tables etc
aldavenport
Member Posts: 2 Learner I
Hello,
I am working on a college project of which involves taking a CSV file and breaking the data by class into different tables. My goal is to then run a probability assessment against these tables and hopefully output a percentage. I am new to RapidMiner and am having some issues with the operators. Does anyone here have any helpful tips? Anything would be greatly appreciated.
I am working on a college project of which involves taking a CSV file and breaking the data by class into different tables. My goal is to then run a probability assessment against these tables and hopefully output a percentage. I am new to RapidMiner and am having some issues with the operators. Does anyone here have any helpful tips? Anything would be greatly appreciated.
Tagged:
0
Answers
If your CSV has something like:
id,att1,att2,class
0,0,0,none
1,1,1,odd
2,2,1,even
3,3,2,odd
4,4,3,even
5,5,5,odd
6,6,8,even
There are many ways to do this. The simple one would be a chain of "Filter Examples" plus "Store". You Read CSV and then apply as many Filter Examples operators as classes you have, and then store these (wherever you want)
A more complex one would be this:
This one multiplies the data from the CSV. First line (on the top) it selects and removes the duplicates of your data, and then it loops over each information related with the class.
(Make sure this is the order you have at this level)
Inside the Loop Examples operator, you can have this:
That uses the Extract Macro operator to filter the examples recalled and store these, no matter how many classes you have.
Hope this helps,
Rodrigo.
Best,
Martin
Dortmund, Germany