Dimensionality Reduction
An approach that reduces the number of variables representing data while preserving as much useful information as possible.
Dimensionality reduction aims to make high-dimensional data more manageable, more efficient to process, and often more meaningful. When data contains too many variables, computational cost rises and the model may become more sensitive to irrelevant noise. Dimensionality reduction techniques create more compact representation spaces while preserving the essence of the information. This provides major benefits for both visualization and modeling. It is especially valuable in high-dimensional embedding spaces and in many classical data science problems.
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