Working With Sparse Features In Machine Learning Models - KDnuggets

Features with sparse data are features that have mostly zero values. This is different from features with missing data. Examples of sparse features include vectors of one-hot-encoded words or counts of categorical data. On the other hand, features with dense data have predominantly non-zero values. When there is missing data, it means that many data points are unknown.