  Understanding and identifying different types of single molecules' diffusion
that occur in a broad range of systems (including living matter) is extremely
important, as it can provide information on the physical and chemical
characteristics of particles' surroundings. In recent years, an ever-growing
number of methods have been proposed to overcome some of the limitations of the
mean-squared displacements approach to tracer diffusion. In March 2020, the
Anomalous Diffusion (AnDi) Challenge was launched by a community of
international scientists to provide a framework for an objective comparison of
the available methods for anomalous diffusion. In this paper, we introduce a
feature-based machine learning method developed in response to Task 2 of the
challenge, i.e. the classification of different types of diffusion. We discuss
two sets of attributes that may be used for the classification of
single-particle tracking data. The first one was proposed as our contribution
to the AnDi Challenge. The latter is the result of our attempt to improve the
performance of the classifier after the deadline of the competition. Extreme
gradient boosting was used as the classification model. Although the
deep-learning approach constitutes the state-of-the-art technology for data
classification in many domains, we deliberately decided to pick this
traditional machine learning algorithm due to its superior interpretability.
After the extension of the feature set our classifier achieved the accuracy of
0.83, which is comparable with the top methods based on neural networks.
