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# Sample Operator - Probability

Member, University Professor Posts: 146 Guru
I have to admit, I am having a hard time understanding the output from a Sample Operator when selecting probability as the sample parameter.

For example, if I use Generate Data to create a 100 example ExampleSet, and I connect the Sample Operator with probability and .1, I get 7 records.

In short, why is it not 10 records?   I am having a hard time wrapping my head around this.

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Member, University Professor Posts: 391 Unicorn
Solution Accepted
Here the sample size is determined in a probabilistic way, from the normal distribution then the sample is selected randomly. I assume this could find its application in repeated resampling to avoid the bias attached to a fixed sample size.

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Member, University Professor Posts: 391 Unicorn
edited January 2021
@btibert you have not been lucky It is a probabilistic sampling so if you were to sample a 1000 times, the 0.1 probability samples drawn would be on average 10 examples. You can try to do the following experiment, in a loop of 1000 times vary the random seed from 0 to 1000 and check the sample size, you will then see a distribution of sample sizes, the more examples to start with and more samples requested the more normal distribution it is going to be. So when you sample once, you may not be lucky to have the sample size perfectly on the mean.
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Member, University Professor Posts: 146 Guru
edited January 2021
@jacobcybulski
Thanks for the help, but I suppose, let me ask this differently.  What exactly is the sampling doing under the hood?  Is it assigning every record a score from a distribution and only selecting those with a value <..1 or > .9?  I am just trying to wrap my head around this approach to sampling.  I tend to compare to R or python where I can set the number of random records, or the % of records I want.  The idea of probabilistic sampling is not something I have come across too often.
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Member, University Professor Posts: 391 Unicorn
An application of such sampling can be in simulation, eg studying the lift operation during the morning peak, where the lifts would be filled to their legal limits but sometimes with a few people less and sometimes with a few people more, over the limit. The samples of lift loads could be taken from a data set of typical people of different gender, weights and heights.
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Member, University Professor Posts: 146 Guru
Thanks!
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Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,525 RM Data Scientist
edited January 2021

I checked the code. What happens is something like this:

`df = df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))r = np.random.uniform(0,1,100)mask = np.where(r<0.1,True,False)df = df[mask]`

in python. There is some more code to make it efficient and so on. But in principal its that. For the rest i just second jacob.

Cheers,
Martin

- Sr. Director Data Solutions, Altair RapidMiner -
Dortmund, Germany
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Member, University Professor Posts: 146 Guru
@mschmitz

That is perfect.  Exactly what I was looking for!