t-SNE, which stands for t-distributed Stochastic Neighbor Embedding, is a popular dimensionality reduction technique (of type Feature Extraction) used in machine learning and data visualization. It is particularly useful for visualizing high-dimensional data in a lower-dimensional space, typically two or three dimensions, while preserving the local structure of the data as much as possible. The…
Principal Component Analysis (PCA) is a widely used linear dimensionality reduction technique (of type Feature Extraction) used for reducing the dimensionality of datasets containing many correlated variables while preserving most of the variability in the data. Here’s how PCA works: Each of the “new” variables after PCA are all independent of one another. PCA has…
Feature Elimination and Feature Extraction are two common techniques used in dimensionality reduction, a process aimed at reducing the number of features (or dimensions) in a dataset while preserving the most important information. Both techniques are used to address the curse of dimensionality, improve computational efficiency, and potentially enhance model performance. However, they differ in…
