I don't think that having that feature is a good idea. The great power of
decision trees (and ensembles of trees) is the ability to learn complicated
non-linearities which involve splitting on a variable multiple times if
necessary. If you're looking for an interpretable feature selection method,
there are better alternatives.
On Wed, Jul 1, 2015 at 8:16 AM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> 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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