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Dimensionality reduction techniques simplify complex datasets from "summary" of Data Science and Big Data Analytics by EMC Education Services
Dimensionality reduction techniques play a crucial role in simplifying complex datasets by reducing the number of features or variables under consideration. In many real-world scenarios, datasets contain a large number of variables, making it difficult to analyze and interpret the data effectively. By reducing the dimensionality of the dataset, data scientists can focus on the most important variables that capture the underlying patterns and relationships within the data. One common approach to dimensionality reduction is Principal Component Analysis (PCA), which aims to transform the original variables into a new set of variables, called principal components, that are linear combinations of the original variables. These principal components are ordered in such a way that the first few components capture the maximum variance in the data. By retaining only a subset of the principal components that explain most of the variance, data sc...
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