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 <> 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

Cheers, Peter
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 577-5304
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