Identifying the attributes que are evaluated in a decision tree

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Identifying the attributes que are evaluated in a decision tree

eclipso
Hi.

Let us suppose that we have trained a decision tree DT in some dataset.
And let us suppose that we have a new instance I that we can classify using DT.

Can we identify, using the weka API, what are the attributes that DT have evaluated for classifying the instance I?

Best regards.

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Re: Identifying the attributes que are evaluated in a decision tree

Peter Reutemann-3
On December 18, 2019 3:08:59 PM GMT+13:00, Marcelino Borges <[hidden email]> wrote:

>Hi.
>
>Let us suppose that we have trained a decision tree DT in some dataset.
>And let us suppose that we have a new instance I that we can classify
>using
>DT.
>
>Can we identify, using the weka API, what are the attributes that DT
>have
>evaluated for classifying the instance I?
>
>Best regards.

Not that I'm aware of, unfortunately.

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 858-5174
http://www.cms.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz/
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Re: Identifying the attributes que are evaluated in a decision tree

Eibe Frank-3
It's not quite the same, but you could alternatively look at how replacing each attribute value in turn with a missing value affects the classifier's predictions.

Another option would be to look at how replacing all *other* attributes with missing values would change predictions when compared to replacing *all* attribute values with missing values.

Cheers,
Eibe

On Wed, Dec 18, 2019 at 9:37 PM Peter Reutemann <[hidden email]> wrote:
On December 18, 2019 3:08:59 PM GMT+13:00, Marcelino Borges <[hidden email]> wrote:
>Hi.
>
>Let us suppose that we have trained a decision tree DT in some dataset.
>And let us suppose that we have a new instance I that we can classify
>using
>DT.
>
>Can we identify, using the weka API, what are the attributes that DT
>have
>evaluated for classifying the instance I?
>
>Best regards.

Not that I'm aware of, unfortunately.

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 858-5174
http://www.cms.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz/
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