High-dimensional Datasets

High-dimensional datasets refer to datasets that have a large number of features or variables compared to the number of observations. In other words, the number of dimensions in the dataset is much higher than the number of samples. High-dimensional datasets are common in many fields, including genomics, image processing, and social network analysis. Analyzing high-dimensional datasets can be challenging due to the curse of dimensionality, which refers to the fact that as the number of dimensions increases, the amount of data required to generalize accurately grows exponentially. Therefore, techniques such as dimensionality reduction, feature selection, and regularization are often used to reduce the number of dimensions and improve the performance of machine learning models on high-dimensional datasets.
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