Examples of rotationally invariant algorithms include logistic regression and MLPs trained using back-propagation (provided that the weights are initialized using independent samples from a spherically symmetric distribution). In contrast, decision trees (which perform feature-axis aligned splits) and, hence, RFs are examples of classifiers that are not rotationally invariant. This behavior can be observed in practice when rotating tabular data: