Straight from the documentation:
[
max_features
] is the size of the random subsets of features to consider when splitting a node.
So max_features
is what you call m. When max_features="auto"
, m = p and no feature subset selection is performed in the trees, so the “random forest” is actually a bagged ensemble of ordinary regression trees. The docs go on to say that
Empirical good default values are
max_features=n_features
for regression problems, andmax_features=sqrt(n_features)
for classification tasks
By setting max_features
differently, you’ll get a “true” random forest.