Re: [scikit-learn] [TC Vote] Technical Committee vote: line length

> Keep current 88 characters: > Revert to 79 characters: Alex Gramfort ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] [TC Vote] Technical Committee vote: line length

> 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 ha

[scikit-learn] random forests and multil-class probability

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, co

Re: [scikit-learn] random forests and multil-class probability

> 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 p

Re: [scikit-learn] random forests and multil-class probability

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 to

Re: [scikit-learn] random forests and multil-class probability

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;

Re: [scikit-learn] random forests and multil-class probability

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 need,

Re: [scikit-learn] random forests and multil-class probability

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: https://scikit-learn.org/stab

Re: [scikit-learn] random forests and multil-class probability

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 de

Re: [scikit-learn] [TC Vote] Technical Committee vote: line length

> > Keep current 88 characters > Joel Nothman (though admittedly not strong!) > > Revert to 79 characters: > Alex Gramfort Adrin Jalali ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn