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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Validate the built model
Date Wed, 06 Sep 2017 13:39:17 GMT
We do cross-validation tests to see how well the model predicts actual behavior. As to the
best data mix, cross-validation works with any engine tuning or data input. Typically this
requires re-traiing between test runs so make sure you use exatly the same training/test split.
If you want to examine the usefulness of different events you can compare event-type 1 to
event type 1 + event type 2 etc. This is made easier by inputting all events, then using a
test trick in the UR to mask out any combination of events for the cross-validation, using
the single existing model so no need to re-train for this type of analysis. We have an un-supported
script that does this but I warn you that you are on your own using it. 

https://github.com/actionml/analysis-tools <https://github.com/actionml/analysis-tools>


On Sep 6, 2017, at 6:15 AM, Saarthak Chandra <chandra.saarthak@gmail.com> wrote:

Hi,

With the Universal Recommender,

1. How can we validate the model after we train and deploy it?

2. How can we find an appropriate method of data mixing ??

Thanks
-- 
Saarthak Chandra,
Masters in Computer Science,
Cornell University.

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