FIG. 32.

Using Random Forests (RFs) to classify Ising Phases. (Top) Accuracy of RFs for classifying the phase of samples from the Ising mode for the training set (blue), test set (red), and critical region (green) using coarse trees with a few leaves (triangles) and fine decision trees with many leaves (filled circles). RFs were trained on samples from ordered and disordered phases but were not trained on samples from the critical region. (Bottom) The time it takes to train RFs scales linearly with the number of estimators in the ensemble. For the upper panel, note that the train case (blue) overlaps with the test case (red). Here ‘fine’ and ‘coarse’ refer to trees with 2 and 10,000 leaves, respectively. For implementation details, see Jupyter notebooks 9