# How to visualize proximity score in Random Forests

For a Random Forest, we can construct a N x N (where N is the number of data points) proximity matrix P where P[i,j] is how "close" the i-th data point is from the j-th data point. In Gilles Loupes' PhD dissertation, he shows an example of a very beautiful proximity visualization using the MNIST dataset:

My question is - how are these proximity plots made? Is there any intuitive difference between that and your traditional distance/similarity matrices? For example, if I have N = 500 data points, should I run the proximity matrix through some sort of dimensionality reduction technique like PCA / SVD / t-SNE so it is of form 500 x 2, and the visualize it?

• Is there no description of how the plot was constructed in the text of the dissertation that you cite?
– Sycorax
May 20, 2019 at 18:10
• There is not.@Sycorax May 20, 2019 at 18:10