  In order to study transport in complex environments, it is extremely
important to determine the physical mechanism underlying diffusion, and
precisely characterize its nature and parameters. Often, this task is strongly
impacted by data consisting of trajectories with short length and limited
localization precision. In this paper, we propose a machine learning method
based on a random forest architecture, which is able to associate even very
short trajectories to the underlying diffusion mechanism with a high accuracy.
In addition, the method is able to classify the motion according to normal or
anomalous diffusion, and determine its anomalous exponent with a small error.
The method provides highly accurate outputs even when working with very short
trajectories and in the presence of experimental noise. We further demonstrate
the application of transfer learning to experimental and simulated data not
included in the training/testing dataset. This allows for a full, high-accuracy
characterization of experimental trajectories without the need of any prior
information.
