An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods. 14 public openml==0.12.2,sklearn==0.18.1 18896 sklearn.tree.tree.ExtraTreeClassifier sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 28 2021-01-13T18:23:05Z 0 sklearn.tree.tree.ExtraTreeClassifier