[scikit-learn] random forests and multil-class probability
Guillaume Lemaître
g.lemaitre58 at gmail.com
Tue Jul 27 05:22:10 EDT 2021
> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn <scikit-learn at 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
>
>
>
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