Visualizing Random Forest with Self-Organising Map

TL;DR

This paper introduces a novel visualization method using Self-Organising Maps to better understand Random Forest models and improve their interpretability and classification accuracy.

Contribution

The paper presents a new SOM-based approach for visualizing RF proximity matrices, enhancing interpretability and accuracy over traditional MDS visualization.

Findings

SOM visualization reveals intrinsic data relationships within RF.

SOM learned with RF proximity matrix outperforms Euclidean-based SOM in accuracy.

The method improves understanding of RF models and their data structures.

Abstract

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.