High-dimensional Data

High-dimensional data refers to datasets that have a large number of features or variables, often much larger than the number of observations. In data science, high-dimensional data can pose significant challenges for analysis and modeling due to the curse of dimensionality, which can lead to overfitting, sparsity, and computational complexity. High-dimensional data can arise in various fields, such as genomics, image processing, social networks, and finance, among others. To address the challenges of high-dimensional data, various techniques have been developed, including dimensionality reduction, feature selection, regularization, and ensemble methods. These techniques aim to reduce the number of features, identify the most relevant ones, and improve the generalization performance of models. High-dimensional data analysis is an active research area in data science and machine learning, with many open problems and opportunities for innovation.
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