  The paper proposes a new variant of a decision tree, called an Extreme
Learning Tree. It consists of an extremely random tree with non-linear data
transformation, and a linear observer that provides predictions based on the
leaf index where the data samples fall. The proposed method outperforms linear
models on a benchmark dataset, and may be a building block for a future variant
of Random Forest.
