High-dimensional Spaces
In data science, high-dimensional spaces refer to datasets with a large number of features or variables. These spaces can be difficult to visualize and analyze due to the curse of dimensionality, which states that as the number of dimensions increases, the amount of data required to maintain the same level of density increases exponentially. High-dimensional spaces can also lead to overfitting, where a model becomes too complex and fits the noise in the data rather than the underlying patterns. To overcome these challenges, techniques such as dimensionality reduction, feature selection, and regularization are used to reduce the number of dimensions and extract the most important features.
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