Yes, other random forest implementations may be using other heuristics for choosing the size of the random subset of attributes considered at each node of the decision tree as it is being built. WEKA's heuristic is close to (but not exactly the same) as the original heuristic that Leo Breiman first proposed when he introduced random forests.

In practice, to squeeze the absolutely best performance out of a random forest, you generally have to tune this parameter anyway (for example, using internal k-fold cross-validation). These heuristics will almost never give you the best possible random forest for your data.

In WEKA, to automatically tune the parameter specifying the subset size using internal k-fold cross-validation, you could use CVParameterSelection or MultiSearch (the latter is available in a separate package).

Cheers,
Eibe

On Fri, Apr 30, 2021 at 11:22 AM Neha gupta <neha.bologna90@gmail.com> wrote:
Thank you Peter, very nice explanation.

In some literature, I read that the 'mtry' parameter of RF is sqrt(number of features) for classification problems and number of features / 3 for regression problems.

Kind regards

On Wed, Apr 28, 2021 at 12:32 AM Peter Reutemann <fracpete@waikato.ac.nz> wrote:
> I am sorry but I did not understand your point. In the more option, I can see
>
> numFeatures -- Sets the number of randomly chosen attributes. If 0,
> int(log_2(#predictors) + 1)
>
> but how can I get the number of variables for each node? I have 20 features in my dataset.

#predictors is the number of attributes without the class. If you have
20 features incl the class, then you get:
int(log_2(19)+1) = 5

Broken down:
log_2(19) ~ 4.25
log_2(19) + 1 ~ 5.25
int(log_2(19)+1) = 5

That's the number of attributes that are randomly chosen for a tree in
RandomForest.

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 577-5304
http://www.cms.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz/
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