If you are working with entirely binary data, then features will not be
repeated in the tree naturally. I think you are discussing the more general
field of 'feature selection', though. There are a plethora of algorithms
which do that--try to identify which inputs are important to a correct
prediction. You can read more here:
http://scikit-learn.org/stable/modules/feature_selection.html
On Wed, Jul 1, 2015 at 9:45 AM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> 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
>
> <image002.jpg> <http://blog.nexidia.com/> <image003.jpg>
> <https://www.linkedin.com/company/nexidia> <image004.jpg>
> <https://plus.google.com/u/0/107921893643164441840/posts> <image005.jpg>
> <https://twitter.com/Nexidia> <image006.jpg>
> <https://www.youtube.com/user/NexidiaTV>
>
> *From:* Sebastian Raschka [mailto:se.rasc...@gmail.com
> <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> 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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