Hi,
I am trying to understand and find out what are the different techniques commonly used to Caps and floor outliers present in a dataset? Any guidance/research paper link is greatly appreciated?
Hi Pritish,
Take a look at the Replacement node in SAS Enterprise miner. It does not remove outliers the way the Filter node would.
By default the limit method is based on a standard deviation from the mean. Take a look at the flooring and capping that happens by default. There is also a brief example of how to use the replacement editor in the reference help (press F1 and go to the Replacement node under the Modify sections).
Good luck!
Miguel
Usually , you can define outliers as if > mean+2*std or < mean-2*std
Thanks! How do you treat those outlier? I don't want them to exclude from my dataset.
Hi Pritish,
Take a look at the Replacement node in SAS Enterprise miner. It does not remove outliers the way the Filter node would.
By default the limit method is based on a standard deviation from the mean. Take a look at the flooring and capping that happens by default. There is also a brief example of how to use the replacement editor in the reference help (press F1 and go to the Replacement node under the Modify sections).
Good luck!
Miguel
I will Winsorted . Replace them with 90th or 95th percentile .
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Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
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