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/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py <https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py> -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/
> On 27 Jul 2021, at 11:42, Sole Galli via scikit-learn > <scikit-learn@python.org> wrote: > > 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, because they can indeed handle multi class, each one in their own way. > > But, if I want to plot PR curves or ROC curves, then I do need to wrap them > because those metrics are calculated as a 1 vs rest manner, and this is not > how it is handled by the algos. Is my understanding correct? > > Thank you! > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ > On Tuesday, July 27th, 2021 at 11:33 AM, Nicolas Hug <nio...@gmail.com> wrote: >> 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 >> <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; >>> >>> Use splitting criteria that compute the average reduction across all n >>> outputs. >>> >> >> >> Nicolas >> >> On 27/07/2021 10:22, Guillaume Lemaître wrote: >>>> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn >>>> <scikit-learn@python.org> <mailto:scikit-learn@python.org> 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 per class under the hood?. >>> Each decision tree of the forest is natively supporting multi class. >>> >>>> The predict_proba output is an array with 3 columns, containing the >>>> probability of each class. If it is 1 vs rest. am I correct to assume that >>>> the sum of the probabilities for the 3 classes should not necessarily add >>>> up to 1? are they normalized? how is it done so that they do add up to 1? >>> According to the above answer, the sum for each row of the array given by >>> `predict_proba` will sum to 1. >>> According to the documentation, the probabilities are computed as: >>> >>> The predicted class probabilities of an input sample are computed as the >>> mean predicted class probabilities of the trees in the forest. The class >>> probability of a single tree is the fraction of samples of the same class >>> in a leaf. >>> >>>> Thank you >>>> Sole >>>> >>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org <mailto:scikit-learn@python.org> >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> <https://mail.python.org/mailman/listinfo/scikit-learn> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org <mailto:scikit-learn@python.org> >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> <https://mail.python.org/mailman/listinfo/scikit-learn> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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