Yes, and thanks for the answers, it was just a random idea.
But in all seriousness, which algorithm would you use for such a task -- here,
the goal is not predictive performance but rather "inference":
I am collaborating with experimentalists who obtained measurements on a
continuous scale 0.0 - 1.0, and each sample has ~30 binary features. They
basically want to "learn" from this data, for example, which combination of
features was "important" to yield a response >= 0.5 (although this threshold is
not fixed)
For example, using a decision tree, you could come up with something like
If feature A=1 --> response > 0.5
If feature B=0 --> response > 0.6
If feature C=1 ---> response > 0.7
etc.
Basically, an association rule mining but with continuous outputs.
> On Jul 1, 2015, at 12:34 PM, Dale Smith <dsm...@nexidia.com> wrote:
>
> 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
>
> <image001.png> <http://nexidia.com/>
>
> d. 404.495.7220 x 4008 f. 404.795.7221
> Nexidia Corporate | 3565 Piedmont Road, Building Two, Suite 400 | Atlanta, GA
> 30305
>
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> <https://www.youtube.com/user/NexidiaTV>
>
> 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
>
> <http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>
>
>
>
>
>
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