  Random Forest (RF) is a powerful ensemble method for classification and
regression tasks. It consists of decision trees set. Although, a single tree is
well interpretable for human, the ensemble of trees is a black-box model. The
popular technique to look inside the RF model is to visualize a RF proximity
matrix obtained on data samples with Multidimensional Scaling (MDS) method.
Herein, we present a novel method based on Self-Organising Maps (SOM) for
revealing intrinsic relationships in data that lay inside the RF used for
classification tasks. We propose an algorithm to learn the SOM with the
proximity matrix obtained from the RF. The visualization of RF proximity matrix
with MDS and SOM is compared. What is more, the SOM learned with the RF
proximity matrix has better classification accuracy in comparison to SOM
learned with Euclidean distance. Presented approach enables better
understanding of the RF and additionally improves accuracy of the SOM.
