Sparse Matrices: Why They Matter for Machine Learning and Data Science

When representing data using a matrix, we can quantify the number of empty values it contains. This is referred to as its sparsity. A matrix (or dataset) that mostly contains 0s is called a sparse matrix. Suppose you ask 4 of your friends to give you a rating of 4 different movies from 1 to 5 (0 if they have not seen it). This means that John has not seen movies 1, 2 and 4 but gave the 3rd one a rating of 2. The sparsity matrix of this matrix is low - 38 % to be precise (6 zeroes out of 16 values 3/8 sparsity) and we would actually call it a "dense" matrix.