How would you use dimensionality reduction to help visualize a complex, high-dimensional dataset?
- Use PCA to reduce to 2 or 3 dimensions
- Increase the number of dimensions for clarity
- Visualize each feature separately
- Apply clustering first
Using PCA to reduce the data to 2 or 3 dimensions is an effective way to visualize complex, high-dimensional datasets. This transformation retains the most significant patterns while making it possible to plot the data in a 2D or 3D space, thus facilitating the understanding of the underlying structure. Other options do not directly contribute to meaningful visualizations of high-dimensional data.
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