Dimensionality Reduction: PCA, t-SNE, and UMAP
In the world of data science, we often encounter datasets with a large number of features. While this wealth of information can be beneficial, it also brings its share of challenges, known as the “curse of dimensionality”. As the number of dimensions increases, the data space becomes vast and sparse, making analysis, visualization, and even training machi...