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<p>To add to Guillaume's answer: the native multiclass support for
forests/trees is described here:
<a class="moz-txt-link-freetext" href="https://scikit-learn.org/stable/modules/tree.html#multi-output-problems">https://scikit-learn.org/stable/modules/tree.html#multi-output-problems</a></p>
<p>It's not a one-vs-rest strategy and can be summed up as:</p>
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<p>Store n output values in leaves, instead of 1;</p>
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<p class="">Use splitting criteria that compute the
average reduction across all
n outputs.</p>
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<p>Nicolas<br>
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<div class="moz-cite-prefix">On 27/07/2021 10:22, Guillaume Lemaître
wrote:<br>
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cite="mid:B3AF592B-2725-4DFF-BE06-718061AD1974@gmail.com">
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<pre class="moz-quote-pre" wrap="">On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn <a class="moz-txt-link-rfc2396E" href="mailto:scikit-learn@python.org"><scikit-learn@python.org></a> 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?.
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Each decision tree of the forest is natively supporting multi class.
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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?
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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.
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Thank you
Sole
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