👉 In data science and machine learning, sparseness refers to the absence of redundant or unnecessary information in a dataset. In other words, it means that there is as much space available for meaningful features as possible within the dataset. For example, if you have a large dataset with many features, but only a few are actually relevant, this can lead to high computational cost and poor performance. By using sparseness techniques, such as dimensionality reduction or feature selection, we can reduce the number of
