Yes, exactly.
Le 12 sept. 2014 18:31, "Luca Puggini" <lucapug...@gmail.com> a écrit :
> Hey thanks a lot,
> so basically in random Forest the split is done like in the algorithm
> described in your thesis except that the search is not done on all the
> variables but only on a random subset of them? (usually sqrt(p) or
> something like that)
>
> Let me know.
> Thanks,
> Luca
>
> Hi Luca,
>>
>> The "best" strategy consists in finding the best threshold, that is the
>> one
>> that maximizes impurity decrease, when trying to partition a node into a
>> left and right nodes. By contrast, "random" does not look for the best
>> split and simply draw the discretization threshold at random.
>>
>> For further details, you can have a look at section 3.6.3 of my thesis. It
>> describes the algorithm as it is implemented in Scikit-Learn.
>> http://www.montefiore.ulg.ac.be/~glouppe/pdf/phd-thesis.pdf
>>
>> Hope this helps,
>> Gilles
>>
>> On 12 September 2014 16:11, Luca Puggini <lucapug...@gmail.com> wrote:
>>
>> > Hi,
>> > I am using random forest classifier and this algorithm train a tree
>> > defined as :
>> >
>> > DecisionTreeClassifier(criterion='gini', max_depth=None,
>> > max_features='auto',
>> > max_leaf_nodes=None, min_samples_leaf=1,
>> min_samples_split=2,
>> > min_weight_fraction_leaf=0.0, random_state=1982007276,
>> > splitter='best')]
>> >
>> > I do not understand what algorithm is used to train a tree with this
>> > parameters.
>> > Is there any reference that describes the used training algorithm in
>> > details?
>> >
>> > In particular I do not understand the split strategy
>> >
>> > splitter : string, optional (default="best")
>> > The strategy used to choose the split at each node. Supported strategies
>> > are "best" to choose the best split and "random" to choose the best
>> > random split.
>> >
>> >
>> > Thanks for help,
>> > Luca
>>
>
>
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