Visualizing Random Forest with Self-Organising Map

Abstract

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.

On the behaviour of permutation‐based variable importance measures in random forest clustering

“…These two methods are referred to as Addcl6 and Addcl5, respectively. After the RF proximity matrix is transformed to a dissimilarity matrix, it can be used as an input for many clustering methods or data reduction techniques for low‐dimensional data visualization, eg, multidimensional scaling or self‐organizing maps …”

Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map

“…3.1, in (1), and the rest of notation is described in (6). The asymmetric similarity defined in this way, using the asymmetric coefficient, guarantees the consistency with the asymmetric hierarchical associations among objects in the dataset.…”

“…where the notation is explained in (4), (6), and (7). The energy function (8) can be optimized in the similar way as the error function (4).…”

“…The self-organizing map (SOM) [1][2][3][4][5][6][7] is an example of the artificial neural network architecture. It was introduced by T. Kohonen in [8] as a generalization and extension of the concepts proposed in [9].…”

Image Segmentation in Liquid Argon Time Projection Chamber Detector

“…The Logistic Regression (LR) [10] and Random Forest [5], [20] algorithms were used as classifiers. They were trained on all features described in Section 2.1 contrary to the Decision Stump (DS) [25] trained only on considered pixel's amplitude value.…”