Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data.

What are dimensionality reduction and its benefits?

Advantages of Dimensionality Reduction

Dimensionality Reduction helps in data compression, and hence reduced storage space. It reduces computation time. It also helps remove redundant features, if any. Dimensionality Reduction helps in data compressing and reducing the storage space required.

Does dimensionality reduction reduce Collinearity?

What are the benefits of Dimension Reduction? It takes care of multi-collinearity that improves the model performance. It removes redundant features. For example: there is no point in storing a value in two different units (meters and inches).