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 <[email protected]> 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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