You may want to use the parameter called "max_features". Indeed:
"1.11.2.3. Parameters -- The main parameters to adjust when using these methods is n_estimators and max_features. The former is the number of trees in the forest. The larger the better, but also the longer it will take to compute. In addition, note that results will stop getting significantly better beyond a critical number of trees. *The latter is the size of the random subsets of features to consider when splitting a node.*" Best regards, Nicolas 2016-09-13 10:15 GMT+02:00 斌洪 <[email protected]>: > I have read the Guide of sklearn's RandomForest : > > """ > In random forests (see RandomForestClassifier > <http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier> > and RandomForestRegressor > <http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor> > classes), each tree in the ensemble is built from a sample drawn with > replacement (i.e., a bootstrap sample) from the training set. > """ > > But I prefer RandomForest as : > """ > features ("attributes", "predictors", "independent variables") are > randomly sampled > """ > > is RandomForest random samples or random features? where can I find a > features random version of RandomForest? > > thx. > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
_______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
