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;
*
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>
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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