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Group-based sampling

I have a data set of users, each who attend a school. I want to filter out the data so that only the data from a random number of schools is displayed. My current method involves using `Select attribute` to select a list of schools (once per attending student), applying `Remove duplicates`, applying sampling the schools, then joining with the original dataset.

Four operations seems rather complicated for such a simple operation, so I was wondering if there was a better way.

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fras

Maybe operator "sample (stratified)" is your friend. Attribute "school" must have role "label".

chrisleong

Thanks for that suggestion. It's helpful because I didn't know how to choose the attribute for a stratified sample before. Unfortunately, what I am trying to do is pick random groups (schools) and select all users in these groups - as opposed to taking a sub-sample with the same distribution (among schools) as the full set.

chrisleong

I also had to add a Filter Examples, with no missing attributes as otherwise it mightn't select the desired number of schools

MariusHelf

Hi Chrisleong,

there are so many "easy operations" around that we decided not to implement all of them into their own operator, as long as it is possible to achieve the same result with a combination of other operators. So what you are doing is the exactly correct way.

Best regards,
Marius