> > Keep current 88 characters
>
Joel Nothman (though admittedly not strong!)
> > Revert to 79 characters:
>
Alex Gramfort
Adrin Jalali
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2021年7月27日(火) 12:03 Guillaume Lemaître :
> As far that I remember, `precision_recall_curve` and `roc_curve` do not
> support multi class. They are design to work only with binary
> classification.
>
Correct, the TPR-FPR curve (ROC) was originally intended for tuning a free
parameter, in signal
As far that I remember, `precision_recall_curve` and `roc_curve` do not support
multi class. They are design to work only with binary classification.
Then, we provide an example for precision-recall that shows one way to compute
precision-recall curve via averaging:
Thank you!
So when in the multiclass document says that for the algorithms that support
intrinsically multiclass, which are listed
[here](https://scikit-learn.org/stable/modules/multiclass.html), when it says
that they do not need to be wrapped by the OnevsRest, it means that there is no
To add to Guillaume's answer: the native multiclass support for
forests/trees is described here:
https://scikit-learn.org/stable/modules/tree.html#multi-output-problems
It's not a one-vs-rest strategy and can be summed up as:
*
Store n output values in leaves, instead of 1;
Thank you!
I was confused because in the multiclass documentation it says that for those
estimators that have multiclass support built in, like Decision trees and
Random Forests, then we do not need to use the wrapper classes like the
OnevsRest.
Thus I have the following question, if I want
> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn
> wrote:
>
> Hello community,
>
> Do I understand correctly that Random Forests are trained as a 1 vs rest when
> the target has more than 2 classes? Say the target takes values 0, 1 and 2,
> then the model would train 3 estimators 1
Hello community,
Do I understand correctly that Random Forests are trained as a 1 vs rest when
the target has more than 2 classes? Say the target takes values 0, 1 and 2,
then the model would train 3 estimators 1 per class under the hood?.
The predict_proba output is an array with 3 columns,
> Keep current 88 characters:
> Revert to 79 characters:
Adrin Jalali
On Tue, Jul 27, 2021 at 1:03 AM Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> This email is meant for the scikit-learn Technical Committee, and is on
> the public mailing list for transparency.
>
> The community
> Keep current 88 characters:
> Revert to 79 characters:
Alex Gramfort
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