It is a crazy idea. It defeats the purpose of random forest, which is
introducing randomness in specific ways in order to achieve certain goals. Your
idea, while appropriate in your use case, does not fit with the algorithm you
want to use. Why not investigate alternatives that better fit your use case?
Dale Smith, Ph.D.
Data Scientist
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From: Sebastian Raschka [mailto:se.rasc...@gmail.com]
Sent: Wednesday, July 01, 2015 12:17 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] Is it possible to specify the order of
spliting in decision tree with scikit-learn?
Maybe a crazy idea, but what I think could be useful is to have something like
a "repeat_features" parameter that can be set to `False` to not reuse features
down the tree.
E.g., let's say we have 1000 different drug molecules with certain chemical
groups and have some sort of experimental data of whether they work or not.
Using decision tree classification/regression without feature repetition could
help to interpret which of the functional groups may be important -- here the
focus is maybe not so much predictive performance but rather interpretability,
something like "supervised" clustering.
On Jul 1, 2015, at 11:08 AM, Andreas Mueller
<t3k...@gmail.com<mailto:t3k...@gmail.com>> wrote:
Not really, at that kind of defeats the purpose of learning the tree.
you could built a series of stumps that first only get feature a, then feature
b and then feature c.
On 06/30/2015 11:37 PM, Rex wrote:
Given three columns, ["A", "B", "C"], can we specify the order of splitting, so
that it firstly split on categories of "A", then "B", and then by others?
Based on on documentation page on DecisionTreeClassifier, there is no such
option. Is there any way to work it out?
http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
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