Dimensionality Reduction
The process of reduction in the number of dimensions (or feature variables) in datasets is known as Dimensionality Reduction.
If a cube has 1000 points, we can reduce its dimensionality by simply taking the 3D data and viewing it as a 2D model. We can also remove feature variables to reduce the data volume. This is generally done with features that have a low correlation with the dataset and is called feature pruning.
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