Hello,
Just wondering, how can we setup path dependant input states for Random
Forest/Decision Tree ?
This is similar to Recurrent Network,
where input Xt=(x0,...,xi,.. yt-1, yt-2) depends on past output states Yt.
If we could put the exact values states Yt, it obviously creates a bias
in the training.
So, we should be put some estimate of (Yt)
Is the concept of Recurrent Tree makes sense ?
Thanks for your insight.
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Thursday, October 20, 2016 1:00 AM
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Today's Topics:
1. Towards 0.18.1 (Joel Nothman)
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Message: 1
Date: Thu, 20 Oct 2016 01:42:31 +1100
From: Joel Nothman <[email protected]>
To: Scikit-learn user and developer mailing list
<[email protected]>
Subject: [scikit-learn] Towards 0.18.1
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Due to a few substantial bugs in 0.18.0, we're hoping to release 0.18.1
around the end of the month. Help solving (and reviewing) the issues
listed
https://github.com/scikit-learn/scikit-learn/milestone/22 is welcome. In
particular, an easy documentation issue at
https://github.com/scikit-learn/scikit-learn/pull/7659 is waiting to be
picked up.
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