A method for ensemble machine learning includes: receiving input data and input models, the input models each having learning properties; generating perturbed data by adding noise to the input data; performing a landmarking operation on the perturbed data to generate meta-features that correlate with the learning properties of the input models; generating decision trees based on the input models and the meta-features.